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RCL: Recurrent Continuous Localization for Temporal Action Detection
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Qiang Wang, Yanhao Zhang, Yun Zheng, Pan Pan
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Temporal representation is the cornerstone of modern action detection techniques. State-of-the-art methods mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the temporal domain with a discretized grid, and then regress the accurate boundaries. In this paper, we revisit this foundational stage and introduce Recurrent Continuous Localization (RCL), which learns a fully continuous anchoring representation. Specifically, the proposed representation builds upon an explicit model conditioned with video embeddings and temporal coordinates, which ensure the capability of detecting segments with arbitrary length. To optimize the continuous representation, we develop an effective scale-invariant sampling strategy and recurrently refine the prediction in subsequent iterations. Our continuous anchoring scheme is fully differentiable, allowing to be seamlessly integrated into existing detectors, e.g., BMN and G-TAD. Extensive experiments on two benchmarks demonstrate that our continuous representation steadily surpasses other discretized counterparts by 2% mAP. As a result, RCL achieves 52.9% [email protected] on THUMOS14 and 37.65% mAP on ActivtiyNet v1.3, outperforming all existing single-model detectors.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RCL_Recurrent_Continuous_Localization_for_Temporal_Action_Detection_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_RCL_Recurrent_Continuous_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.07112
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_RCL_Recurrent_Continuous_Localization_for_Temporal_Action_Detection_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_RCL_Recurrent_Continuous_Localization_for_Temporal_Action_Detection_CVPR_2022_paper.html
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CVPR 2022
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C2SLR: Consistency-Enhanced Continuous Sign Language Recognition
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Ronglai Zuo, Brian Mak
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The backbone of most deep-learning-based continuous sign language recognition (CSLR) models consists of a visual module, a sequential module, and an alignment module. However, such CSLR backbones are hard to be trained sufficiently with a single connectionist temporal classification loss. In this work, we propose two auxiliary constraints to enhance the CSLR backbones from the perspective of consistency. The first constraint aims to enhance the visual module, which easily suffers from the insufficient training problem. Specifically, since sign languages convey information mainly with signers' faces and hands, we insert a keypoint-guided spatial attention module into the visual module to enforce it to focus on informative regions, i.e., spatial attention consistency. Nevertheless, only enhancing the visual module may not fully exploit the power of the backbone. Motivated by that both the output features of the visual and sequential modules represent the same sentence, we further impose a sentence embedding consistency constraint between them to enhance the representation power of both the features. Experimental results over three representative backbones validate the effectiveness of the two constraints. More remarkably, with a transformer-based backbone, our model achieves state-of-the-art or competitive performance on three benchmarks, PHOENIX-2014, PHOENIX-2014-T, and CSL.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zuo_C2SLR_Consistency-Enhanced_Continuous_Sign_Language_Recognition_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zuo_C2SLR_Consistency-Enhanced_Continuous_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zuo_C2SLR_Consistency-Enhanced_Continuous_Sign_Language_Recognition_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zuo_C2SLR_Consistency-Enhanced_Continuous_Sign_Language_Recognition_CVPR_2022_paper.html
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CVPR 2022
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Human Trajectory Prediction With Momentary Observation
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Jianhua Sun, Yuxuan Li, Liang Chai, Hao-Shu Fang, Yong-Lu Li, Cewu Lu
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Human trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars and social robots. In real-world scenarios, it is unlikely to obtain sufficiently long observations at all times for prediction, considering inevitable factors such as tracking losses and sudden events. However, the problem of trajectory prediction with limited observations has not drawn much attention in previous work. In this paper, we study a task named momentary trajectory prediction, which reduces the observed history from a long time sequence to an extreme situation of two frames, one frame for social and scene contexts and both frames for the velocity of agents. We perform a rigorous study of existing state-of-the-art approaches in this challenging setting on two widely used benchmarks. We further propose a unified feature extractor, along with a novel pre-training mechanism, to capture effective information within the momentary observation. Our extractor can be adopted in existing prediction models and substantially boost their performance of momentary trajectory prediction. We hope our work will pave the way for more responsive, precise and robust prediction approaches, an important step toward real-world autonomous systems.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Human_Trajectory_Prediction_With_Momentary_Observation_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Human_Trajectory_Prediction_With_Momentary_Observation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Human_Trajectory_Prediction_With_Momentary_Observation_CVPR_2022_paper.html
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CVPR 2022
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FoggyStereo: Stereo Matching With Fog Volume Representation
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Chengtang Yao, Lidong Yu
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Stereo matching in foggy scenes is challenging as the scattering effect of fog blurs the image and makes the matching ambiguous. Prior methods deem the fog as noise and discard it before matching. Different from them, we propose to explore depth hints from fog and improve stereo matching via these hints. The exploration of depth hints is designed from the perspective of rendering. The rendering is conducted by reversing the atmospheric scattering process and removing the fog within a selected depth range. The quality of the rendered image reflects the correctness of the selected depth, as the closer it is to the real depth, the clearer the rendered image is. We introduce a fog volume representation to collect these depth hints from the fog. We construct the fog volume by stacking images rendered with depths computed from disparity candidates that are also used to build the cost volume. We fuse the fog volume with cost volume to rectify the ambiguous matching caused by fog. Experiments show that our fog volume representation significantly promotes the SOTA result on foggy scenes by 10% ~ 30% while maintaining a comparable performance in clear scenes.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Yao_FoggyStereo_Stereo_Matching_With_Fog_Volume_Representation_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yao_FoggyStereo_Stereo_Matching_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Yao_FoggyStereo_Stereo_Matching_With_Fog_Volume_Representation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Yao_FoggyStereo_Stereo_Matching_With_Fog_Volume_Representation_CVPR_2022_paper.html
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CVPR 2022
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Trajectory Optimization for Physics-Based Reconstruction of 3D Human Pose From Monocular Video
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Erik Gärtner, Mykhaylo Andriluka, Hongyi Xu, Cristian Sminchisescu
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We focus on the task of estimating a physically plausible articulated human motion from monocular video. Existing approaches that do not consider physics often produce temporally inconsistent output with motion artifacts, while state-of-the-art physics-based approaches have either been shown to work only in controlled laboratory conditions or consider simplified body-ground contact limited to feet. This paper explores how these shortcomings can be addressed by directly incorporating a fully-featured physics engine into the pose estimation process. Given an uncontrolled, real-world scene as input, our approach estimates the ground-plane location and the dimensions of the physical body model. It then recovers the physical motion by performing trajectory optimization. The advantage of our formulation is that it readily generalizes to a variety of scenes that might have diverse ground properties and supports any form of self-contact and contact between the articulated body and scene geometry. We show that our approach achieves competitive results with respect to existing physics-based methods on the Human3.6M benchmark, while being directly applicable without re-training to more complex dynamic motions from the AIST benchmark and to uncontrolled internet videos.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Gartner_Trajectory_Optimization_for_Physics-Based_Reconstruction_of_3D_Human_Pose_From_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gartner_Trajectory_Optimization_for_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Gartner_Trajectory_Optimization_for_Physics-Based_Reconstruction_of_3D_Human_Pose_From_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Gartner_Trajectory_Optimization_for_Physics-Based_Reconstruction_of_3D_Human_Pose_From_CVPR_2022_paper.html
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CVPR 2022
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Directional Self-Supervised Learning for Heavy Image Augmentations
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Yalong Bai, Yifan Yang, Wei Zhang, Tao Mei
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Despite the large augmentation family, only a few cherry-picked robust augmentation policies are beneficial to self-supervised image representation learning. In this paper, we propose a directional self-supervised learning paradigm (DSSL), which is compatible with significantly more augmentations. Specifically, we adapt heavy augmentation policies after the views lightly augmented by standard augmentations, to generate harder view (HV). HV usually has a higher deviation from the original image than the lightly augmented standard view (SV). Unlike previous methods equally pairing all augmented views to symmetrically maximize their similarities, DSSL treats augmented views of the same instance as a partially ordered set (with directions as SV\leftrightarrow SV, SV\leftarrowHV), and then equips a directional objective function respecting to the derived relationships among views. DSSL can be easily implemented with a few lines of codes and is highly flexible to popular self-supervised learning frameworks, including SimCLR, SimSiam, BYOL. Extensive experimental results on CIFAR and ImageNet demonstrated that DSSL can stably improve various baselines with compatibility to a wider range of augmentations. Code is available at: https://github.com/Yif-Yang/DSSL.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Bai_Directional_Self-Supervised_Learning_for_Heavy_Image_Augmentations_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bai_Directional_Self-Supervised_Learning_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2110.13555
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Directional_Self-Supervised_Learning_for_Heavy_Image_Augmentations_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Directional_Self-Supervised_Learning_for_Heavy_Image_Augmentations_CVPR_2022_paper.html
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CVPR 2022
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Lifelong Unsupervised Domain Adaptive Person Re-Identification With Coordinated Anti-Forgetting and Adaptation
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Zhipeng Huang, Zhizheng Zhang, Cuiling Lan, Wenjun Zeng, Peng Chu, Quanzeng You, Jiang Wang, Zicheng Liu, Zheng-Jun Zha
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Unsupervised domain adaptive person re-identification (ReID) has been extensively investigated to mitigate the adverse effects of domain gaps. Those works assume the target domain data can be accessible all at once. However, for the real-world streaming data, this hinders the timely adaptation to changing data statistics and sufficient exploitation of increasing samples. In this paper, to address more practical scenarios, we propose a new task, Lifelong Unsupervised Domain Adaptive (LUDA) person ReID. This is challenging because it requires the model to continuously adapt to unlabeled data in the target environments while alleviating catastrophic forgetting for such a fine-grained person retrieval task. We design an effective scheme for this task, dubbed CLUDA-ReID, where the anti-forgetting is harmoniously coordinated with the adaptation. Specifically, a meta-based Coordinated Data Replay strategy is proposed to replay old data and update the network with a coordinated optimization direction for both adaptation and memorization. Moreover, we propose Relational Consistency Learning for old knowledge distillation/inheritance in line with the objective of retrieval-based tasks. We set up two evaluation settings to simulate the practical application scenarios. Extensive experiments demonstrate the effectiveness of our CLUDA-ReID for both scenarios with stationary target streams and scenarios with dynamic target streams.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Lifelong_Unsupervised_Domain_Adaptive_Person_Re-Identification_With_Coordinated_Anti-Forgetting_and_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Lifelong_Unsupervised_Domain_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.06632
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Lifelong_Unsupervised_Domain_Adaptive_Person_Re-Identification_With_Coordinated_Anti-Forgetting_and_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Lifelong_Unsupervised_Domain_Adaptive_Person_Re-Identification_With_Coordinated_Anti-Forgetting_and_CVPR_2022_paper.html
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CVPR 2022
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No-Reference Point Cloud Quality Assessment via Domain Adaptation
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Qi Yang, Yipeng Liu, Siheng Chen, Yiling Xu, Jun Sun
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We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds. For quality assessment, deep neural network (DNN) has shown compelling performance on no-reference metric design. However, the most challenging issue for no-reference PCQA is that we lack large-scale subjective databases to drive robust networks. Our motivation is that the human visual system (HVS) is the decision-maker regardless of the type of media for quality assessment. Leveraging the rich subjective scores of the natural images, we can quest the evaluation criteria of human perception via DNN and transfer the capability of prediction to 3D point clouds. In particular, we treat natural images as the source domain and point clouds as the target domain, and infer point cloud quality via unsupervised adversarial domain adaptation. To extract effective latent features and minimize the domain discrepancy, we propose a hierarchical feature encoder and a conditional-discriminative network. Considering that the ultimate purpose is regressing objective score, we introduce a novel conditional cross entropy loss in the conditional-discriminative network to penalize the negative samples which hinder the convergence of the quality regression network. Experimental results show that the proposed method can achieve higher performance than traditional no-reference metrics, even comparable results with full-reference metrics. The proposed method also suggests the feasibility of assessing the quality of specific media content without the expensive and cumbersome subjective evaluations. Code is available at https://github.com/Qi-Yangsjtu/IT-PCQA.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_No-Reference_Point_Cloud_Quality_Assessment_via_Domain_Adaptation_CVPR_2022_paper.pdf
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http://arxiv.org/abs/2112.02851
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Yang_No-Reference_Point_Cloud_Quality_Assessment_via_Domain_Adaptation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Yang_No-Reference_Point_Cloud_Quality_Assessment_via_Domain_Adaptation_CVPR_2022_paper.html
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CVPR 2022
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Generating Representative Samples for Few-Shot Classification
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Jingyi Xu, Hieu Le
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Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot class representations are often biased due to data scarcity. To mitigate this issue, we propose to generate visual samples based on semantic embeddings using a conditional variational autoencoder (CVAE) model. We train this CVAE model on base classes and use it to generate features for novel classes. More importantly, we guide this VAE to strictly generate representative samples by removing non-representative samples from the base training set when training the CVAE model. We show that this training scheme enhances the representativeness of the generated samples and therefore, improves the few-shot classification results. Experimental results show that our method improves three FSL baseline methods by substantial margins, achieving state-of-the-art few-shot classification performance on miniImageNet and tieredImageNet datasets for both 1-shot and 5-shot settings.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Generating_Representative_Samples_for_Few-Shot_Classification_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Generating_Representative_Samples_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2205.02918
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Generating_Representative_Samples_for_Few-Shot_Classification_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Generating_Representative_Samples_for_Few-Shot_Classification_CVPR_2022_paper.html
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CVPR 2022
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Comprehending and Ordering Semantics for Image Captioning
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Yehao Li, Yingwei Pan, Ting Yao, Tao Mei
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Comprehending the rich semantics in an image and ordering them in linguistic order are essential to compose a visually-grounded and linguistically coherent description for image captioning. Modern techniques commonly capitalize on a pre-trained object detector/classifier to mine the semantics in an image, while leaving the inherent linguistic ordering of semantics under-exploited. In this paper, we propose a new recipe of Transformer-style structure, namely Comprehending and Ordering Semantics Networks (COS-Net), that novelly unifies an enriched semantic comprehending and a learnable semantic ordering processes into a single architecture. Technically, we initially utilize a cross-modal retrieval model to search the relevant sentences of each image, and all words in the searched sentences are taken as primary semantic cues. Next, a novel semantic comprehender is devised to filter out the irrelevant semantic words in primary semantic cues, and meanwhile infer the missing relevant semantic words visually grounded in the image. After that, we feed all the screened and enriched semantic words into a semantic ranker, which learns to allocate all semantic words in linguistic order as humans. Such sequence of ordered semantic words are further integrated with visual tokens of images to trigger sentence generation. Empirical evidences show that COS-Net clearly surpasses the state-of-the-art approaches on COCO and achieves to-date the best CIDEr score of 141.1% on Karpathy test split. Source code is available at https://github.com/YehLi/xmodaler/tree/master/configs/image_caption/cosnet.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Comprehending_and_Ordering_Semantics_for_Image_Captioning_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_Comprehending_and_Ordering_Semantics_for_Image_Captioning_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_Comprehending_and_Ordering_Semantics_for_Image_Captioning_CVPR_2022_paper.html
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CVPR 2022
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Dynamic Scene Graph Generation via Anticipatory Pre-Training
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Yiming Li, Xiaoshan Yang, Changsheng Xu
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Humans can not only see the collection of objects in visual scenes, but also identify the relationship between objects. The visual relationship in the scene can be abstracted into the semantic representation of triple <subject, predicate, object> and thus results in a scene graph, which can convey a lot of information for visual understanding. Due to the motion of objects, the visual relationship between two objects in videos may vary, which makes the task of dynamically generating scene graphs from videos more complicated and challenging than the conventional image-based static scene graph generation. Inspired by the ability of humans to infer the visual relationship, we propose a novel anticipatory pre-training paradigm based on Transformer to explicitly model the temporal correlation of visual relationships in different frames to improve dynamic scene graph generation. In pre-training stage, the model predicts the visual relationships of current frame based on the previous frames by extracting intra-frame spatial information with a spatial encoder and inter-frame temporal correlations with a temporal encoder. In the fine-tuning stage, we reuse the spatial encoder and the temporal decoder and combine the information of the current frame to predict the visual relationship. Extensive experiments demonstrate that our method achieves state-of-the-art performance on Action Genome dataset.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Dynamic_Scene_Graph_Generation_via_Anticipatory_Pre-Training_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_Dynamic_Scene_Graph_Generation_via_Anticipatory_Pre-Training_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_Dynamic_Scene_Graph_Generation_via_Anticipatory_Pre-Training_CVPR_2022_paper.html
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CVPR 2022
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A Large-Scale Comprehensive Dataset and Copy-Overlap Aware Evaluation Protocol for Segment-Level Video Copy Detection
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Sifeng He, Xudong Yang, Chen Jiang, Gang Liang, Wei Zhang, Tan Pan, Qing Wang, Furong Xu, Chunguang Li, JinXiong Liu, Hui Xu, Kaiming Huang, Yuan Cheng, Feng Qian, Xiaobo Zhang, Lei Yang
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In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset. Compared with existing copy detection datasets restricted by either video-level annotation or small-scale, VCSL not only has two orders of magnitude more segment-level labelled data, with 160k realistic video copy pairs containing more than 280k localized copied segment pairs, but also covers a variety of video categories and a wide range of video duration. All the copied segments inside each collected video pair are manually extracted and accompanied by precisely annotated starting and ending timestamps. Alongside the dataset, we also propose a novel evaluation protocol that better measures the prediction accuracy of copy overlapping segments between a video pair and shows improved adaptability in different scenarios. By benchmarking several baseline and state-of-the-art segment-level video copy detection methods with the proposed dataset and evaluation metric, we provide a comprehensive analysis that uncovers the strengths and weaknesses of current approaches, hoping to open up promising directions for future works. The VCSL dataset, metric and benchmark codes are all publicly available at https://github.com/alipay/VCSL.
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https://openaccess.thecvf.com/content/CVPR2022/papers/He_A_Large-Scale_Comprehensive_Dataset_and_Copy-Overlap_Aware_Evaluation_Protocol_for_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_A_Large-Scale_Comprehensive_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.02654
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/He_A_Large-Scale_Comprehensive_Dataset_and_Copy-Overlap_Aware_Evaluation_Protocol_for_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/He_A_Large-Scale_Comprehensive_Dataset_and_Copy-Overlap_Aware_Evaluation_Protocol_for_CVPR_2022_paper.html
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CVPR 2022
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GaTector: A Unified Framework for Gaze Object Prediction
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Binglu Wang, Tao Hu, Baoshan Li, Xiaojuan Chen, Zhijie Zhang
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Gaze object prediction is a newly proposed task that aims to discover the objects being stared at by humans. It is of great application significance but still lacks a unified solution framework. An intuitive solution is to incorporate an object detection branch into an existing gaze prediction method. However, previous gaze prediction methods usually use two different networks to extract features from scene image and head image, which would lead to heavy network architecture and prevent each branch from joint optimization. In this paper, we build a novel framework named GaTector to tackle the gaze object prediction problem in a unified way. Particularly, a specific-general-specific (SGS) feature extractor is firstly proposed to utilize a shared backbone to extract general features for both scene and head images. To better consider the specificity of inputs and tasks, SGS introduces two input-specific blocks before the shared backbone and three task-specific blocks after the shared backbone. Specifically, a novel Defocus layer is designed to generate object-specific features for the object detection task without losing information or requiring extra computations. Moreover, the energy aggregation loss is introduced to guide the gaze heatmap to concentrate on the stared box. In the end, we propose a novel wUoC metric that can reveal the difference between boxes even when they share no overlapping area. Extensive experiments on the GOO dataset verify the superiority of our method in all three tracks, i.e. object detection, gaze estimation, and gaze object prediction.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_GaTector_A_Unified_Framework_for_Gaze_Object_Prediction_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_GaTector_A_Unified_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.03549
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_GaTector_A_Unified_Framework_for_Gaze_Object_Prediction_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_GaTector_A_Unified_Framework_for_Gaze_Object_Prediction_CVPR_2022_paper.html
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CVPR 2022
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ELIC: Efficient Learned Image Compression With Unevenly Grouped Space-Channel Contextual Adaptive Coding
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Dailan He, Ziming Yang, Weikun Peng, Rui Ma, Hongwei Qin, Yan Wang
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Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders. They are promising to be large-scale adopted. For the sake of practicality, a thorough investigation of the architecture design of learned image compression, regarding both compression performance and running speed, is essential. In this paper, we first propose uneven channel-conditional adaptive coding, motivated by the observation of energy compaction in learned image compression. Combining the proposed uneven grouping model with existing context models, we obtain a spatial-channel contextual adaptive model to improve the coding performance without damage to running speed. Then we study the structure of the main transform and propose an efficient model, ELIC, to achieve state-of-the-art speed and compression ability. With superior performance, the proposed model also supports extremely fast preview decoding and progressive decoding, which makes the coming application of learning-based image compression more promising.
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https://openaccess.thecvf.com/content/CVPR2022/papers/He_ELIC_Efficient_Learned_Image_Compression_With_Unevenly_Grouped_Space-Channel_Contextual_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_ELIC_Efficient_Learned_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.10886
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/He_ELIC_Efficient_Learned_Image_Compression_With_Unevenly_Grouped_Space-Channel_Contextual_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/He_ELIC_Efficient_Learned_Image_Compression_With_Unevenly_Grouped_Space-Channel_Contextual_CVPR_2022_paper.html
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CVPR 2022
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CSWin Transformer: A General Vision Transformer Backbone With Cross-Shaped Windows
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Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, Baining Guo
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We present CSWin Transformer, an efficient and effective Transformer-based backbone for general-purpose vision tasks. A challenging issue in Transformer design is that global self-attention is very expensive to compute whereas local self-attention often limits the field of interactions of each token. To address this issue, we develop the Cross-Shaped Window self-attention mechanism for computing self-attention in the horizontal and vertical stripes in parallel that form a cross-shaped window, with each stripe obtained by splitting the input feature into stripes of equal width. We provide a mathematical analysis of the effect of the stripe width and vary the stripe width for different layers of the Transformer network which achieves strong modeling capability while limiting the computation cost. We also introduce Locally-enhanced Positional Encoding (LePE), which handles the local positional information better than existing encoding schemes. LePE naturally supports arbitrary input resolutions and is thus especially effective and friendly for downstream tasks. Incorporated with these designs and a hierarchical structure, CSWin Transformer demonstrates competitive performance on common vision tasks. Specifically, it achieves 85.4% Top-1 accuracy on ImageNet-1K without any extra training data or label, 53.9 box AP and 46.4 mask AP on the COCO detection task, and 51.7 mIOU on the ADE20K semantic segmentation task, surpassing previous state-of-the-art Swin Transformer backbone by +1.2, +2.0, +1.4, and +2.0 respectively under the similar FLOPs setting. By further pretraining on the larger dataset ImageNet-21K, we achieve 87.5% Top-1 accuracy on ImageNet-1K and state-of-the-art segmentation performance on ADE20K with 55.7 mIoU.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_CSWin_Transformer_A_General_Vision_Transformer_Backbone_With_Cross-Shaped_Windows_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_CSWin_Transformer_A_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2107.00652
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Dong_CSWin_Transformer_A_General_Vision_Transformer_Backbone_With_Cross-Shaped_Windows_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Dong_CSWin_Transformer_A_General_Vision_Transformer_Backbone_With_Cross-Shaped_Windows_CVPR_2022_paper.html
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CVPR 2022
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LaTr: Layout-Aware Transformer for Scene-Text VQA
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Ali Furkan Biten, Ron Litman, Yusheng Xie, Srikar Appalaraju, R. Manmatha
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We propose a novel multimodal architecture for Scene Text Visual Question Answering (STVQA), named Layout-Aware Transformer (LaTr). The task of STVQA requires models to reason over different modalities. Thus, we first investigate the impact of each modality, and reveal the importance of the language module, especially when enriched with layout information. Accounting for this, we propose a single objective pre-training scheme that requires only text and spatial cues. We show that applying this pre-training scheme on scanned documents has certain advantages over using natural images, despite the domain gap. Scanned documents are easy to procure, text-dense and have a variety of layouts, helping the model learn various spatial cues (e.g. left-of, below etc.) by tying together language and layout information. Compared to existing approaches, our method performs vocabulary-free decoding and, as shown, generalizes well beyond the training vocabulary. We further demonstrate that LaTr improves robustness towards OCR errors, a common reason for failure cases in STVQA. In addition, by leveraging a vision transformer, we eliminate the need for an external object detector. LaTr outperforms state-of-the-art STVQA methods on multiple datasets. In particular, +7.6% on TextVQA, +10.8% on ST-VQA and +4.0% on OCR-VQA (all absolute accuracy numbers).
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https://openaccess.thecvf.com/content/CVPR2022/papers/Biten_LaTr_Layout-Aware_Transformer_for_Scene-Text_VQA_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Biten_LaTr_Layout-Aware_Transformer_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.12494
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Biten_LaTr_Layout-Aware_Transformer_for_Scene-Text_VQA_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Biten_LaTr_Layout-Aware_Transformer_for_Scene-Text_VQA_CVPR_2022_paper.html
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CVPR 2022
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Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification
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Jingzhou Chen, Peng Wang, Jian Liu, Yuntao Qian
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Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity labels to each object and focuses on encoding the label hierarchy, e.g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels. However, the definition of what is fine-grained is subjective, and the image quality may affect the identification. Thus, samples could be observed at any level of the hierarchy, e.g., ["Albatross"] or ["Albatross", "Laysan Albatross"], and examples discerned at coarse categories are often neglected in the conventional setting of HMC. In this paper, we study the HMC problem in which objects are labeled at any level of the hierarchy. The essential designs of the proposed method are derived from two motivations: (1) learning with objects labeled at various levels should transfer hierarchical knowledge between levels; (2) lower-level classes should inherit attributes related to upper-level superclasses. The proposed combinatorial loss maximizes the marginal probability of the observed ground truth label by aggregating information from related labels defined in the tree hierarchy. If the observed label is at the leaf level, the combinatorial loss further imposes the multi-class cross-entropy loss to increase the weight of fine-grained classification loss. Considering the hierarchical feature interaction, we propose a hierarchical residual network (HRN), in which granularity-specific features from parent levels acting as residual connections are added to features of children levels. Experiments on three commonly used datasets demonstrate the effectiveness of our approach compared to the state-of-the-art HMC approaches. The code will be available at https://github.com/MonsterZhZh/HRN.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Label_Relation_Graphs_Enhanced_Hierarchical_Residual_Network_for_Hierarchical_Multi-Granularity_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Label_Relation_Graphs_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2201.03194
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Label_Relation_Graphs_Enhanced_Hierarchical_Residual_Network_for_Hierarchical_Multi-Granularity_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Label_Relation_Graphs_Enhanced_Hierarchical_Residual_Network_for_Hierarchical_Multi-Granularity_CVPR_2022_paper.html
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CVPR 2022
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ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks
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WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad, Alireza Bab-Hadiashar, David Suter
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State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains. In this paper, we attempt to unfold an important factor that hinders the networks from generalizing across domains: through the lens of shortcut learning. We demonstrate that the learning of feature representations in stereo matching networks is heavily influenced by synthetic data artefacts (shortcut attributes). To mitigate this issue, we propose an Information-Theoretic Shortcut Avoidance (ITSA) approach to automatically restrict shortcut-related information from being encoded into the feature representations. As a result, our proposed method learns robust and shortcut-invariant features by minimizing the sensitivity of latent features to input variations. To avoid the prohibitive computational cost of direct input sensitivity optimization, we propose an effective yet feasible algorithm to achieve robustness. We show that using this method, state-of-the-art stereo matching networks that are trained purely on synthetic data can effectively generalize to challenging and previously unseen real data scenarios. Importantly, the proposed method enhances the robustness of the synthetic trained networks to the point that they outperform their fine-tuned counterparts (on real data) for challenging out-of-domain stereo datasets.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Chuah_ITSA_An_Information-Theoretic_Approach_to_Automatic_Shortcut_Avoidance_and_Domain_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chuah_ITSA_An_Information-Theoretic_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2201.02263
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Chuah_ITSA_An_Information-Theoretic_Approach_to_Automatic_Shortcut_Avoidance_and_Domain_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Chuah_ITSA_An_Information-Theoretic_Approach_to_Automatic_Shortcut_Avoidance_and_Domain_CVPR_2022_paper.html
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CVPR 2022
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Enhancing Face Recognition With Self-Supervised 3D Reconstruction
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Mingjie He, Jie Zhang, Shiguang Shan, Xilin Chen
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Attributed to both the development of deep networks and abundant data, automatic face recognition (FR) has quickly reached human-level capacity in the past few years. However, the FR problem is not perfectly solved in case of uncontrolled illumination and pose. In this paper, we propose to enhance face recognition with a bypass of self-supervised 3D reconstruction, which enforces the neural backbone to focus on the identity-related depth and albedo information while neglects the identity-irrelevant pose and illumination information. Specifically, inspired by the physical model of image formation, we improve the backbone FR network by introducing a 3D face reconstruction loss with two auxiliary networks. The first one estimates the pose and illumination from the input face image while the second one decodes the canonical depth and albedo from the intermediate feature of the FR backbone network. The whole network is trained in end-to-end manner with both classic face identification loss and the loss of 3D face reconstruction with the physical parameters. In this way, the self-supervised reconstruction acts as a regularization that enables the recognition network to understand faces in 3D view, and the learnt features are forced to encode more information of canonical facial depth and albedo, which is more intrinsic and beneficial to face recognition. Extensive experimental results on various face recognition benchmarks show that, without any cost of extra annotations and computations, our method outperforms state-of-the-art ones. Moreover, the learnt representations can also well generalize to other face-related downstream tasks such as the facial attribute recognition with limited labeled data.
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https://openaccess.thecvf.com/content/CVPR2022/papers/He_Enhancing_Face_Recognition_With_Self-Supervised_3D_Reconstruction_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/He_Enhancing_Face_Recognition_With_Self-Supervised_3D_Reconstruction_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/He_Enhancing_Face_Recognition_With_Self-Supervised_3D_Reconstruction_CVPR_2022_paper.html
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CVPR 2022
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HeadNeRF: A Real-Time NeRF-Based Parametric Head Model
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Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, Juyong Zhang
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In this paper, we propose HeadNeRF, a novel NeRF-based parametric head model that integrates the neural radiance field to the parametric representation of the human head. It can render high fidelity head images in real-time on modern GPUs, and supports directly controlling the generated images' rendering pose and various semantic attributes. Different from existing related parametric models, we use the neural radiance fields as a novel 3D proxy instead of the traditional 3D textured mesh, which makes that HeadNeRF is able to generate high fidelity images. However, the computationally expensive rendering process of the original NeRF hinders the construction of the parametric NeRF model. To address this issue, we adopt the strategy of integrating 2D neural rendering to the rendering process of NeRF and design novel loss terms. As a result, the rendering speed of HeadNeRF can be significantly accelerated, and the rendering time of one frame is reduced from 5s to 25ms. The well designed loss terms also improve the rendering accuracy, and the fine-level details of the human head, such as the gaps between teeth, wrinkles, and beards, can be represented and synthesized by HeadNeRF. Extensive experimental results and several applications demonstrate its effectiveness. The trained parametric model is available at https://github.com/CrisHY1995/headnerf.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_HeadNeRF_A_Real-Time_NeRF-Based_Parametric_Head_Model_CVPR_2022_paper.pdf
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http://arxiv.org/abs/2112.05637
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Hong_HeadNeRF_A_Real-Time_NeRF-Based_Parametric_Head_Model_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Hong_HeadNeRF_A_Real-Time_NeRF-Based_Parametric_Head_Model_CVPR_2022_paper.html
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CVPR 2022
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FvOR: Robust Joint Shape and Pose Optimization for Few-View Object Reconstruction
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Zhenpei Yang, Zhile Ren, Miguel Angel Bautista, Zaiwei Zhang, Qi Shan, Qixing Huang
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Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. State-of-the-art approaches typically assume accurate camera poses as input, which could be difficult to obtain in realistic settings. In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses. The core of our approach is a fast and robust multi-view reconstruction algorithm to jointly refine 3D geometry and camera pose estimation using learnable neural network modules. We provide a thorough benchmark of state-of-the-art approaches for this problem on ShapeNet. Our approach achieves best-in-class results. It is also two orders of magnitude faster than the recent optimization-based approach IDR.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_FvOR_Robust_Joint_Shape_and_Pose_Optimization_for_Few-View_Object_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_FvOR_Robust_Joint_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2205.07763
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Yang_FvOR_Robust_Joint_Shape_and_Pose_Optimization_for_Few-View_Object_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Yang_FvOR_Robust_Joint_Shape_and_Pose_Optimization_for_Few-View_Object_CVPR_2022_paper.html
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CVPR 2022
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Reduce Information Loss in Transformers for Pluralistic Image Inpainting
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Qiankun Liu, Zhentao Tan, Dongdong Chen, Qi Chu, Xiyang Dai, Yinpeng Chen, Mengchen Liu, Lu Yuan, Nenghai Yu
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Transformers have achieved great success in pluralistic image inpainting recently. However, we find existing transformer based solutions regard each pixel as a token, thus suffer from information loss issue from two aspects: 1) They downsample the input image into much lower resolutions for efficiency consideration, incurring information loss and extra misalignment for the boundaries of masked regions. 2) They quantize 2563 RGB pixels to a small number (such as 512) of quantized pixels. The indices of quantized pixels are used as tokens for the inputs and prediction targets of transformer. Although an extra CNN network is used to upsample and refine the low-resolution results, it is difficult to retrieve the lost information back. To keep input information as much as possible, we propose a new transformer based framework "PUT". Specifically, to avoid input downsampling while maintaining the computation efficiency, we design a patch-based auto-encoder PVQVAE, where the encoder converts the masked image into non-overlapped patch tokens and the decoder recovers the masked regions from the inpainted tokens while keeping the unmasked regions unchanged. To eliminate the information loss caused by quantization, an Un-Quantized Transformer (UQ-Transformer) is applied, which directly takes the features from P-VQVAE encoder as input without quantization and regards the quantized tokens only as prediction targets. Extensive experiments show that PUT greatly outperforms state-of-the-art methods on image fidelity, especially for large masked regions and complex large-scale datasets.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Reduce_Information_Loss_in_Transformers_for_Pluralistic_Image_Inpainting_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Reduce_Information_Loss_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2205.05076
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Reduce_Information_Loss_in_Transformers_for_Pluralistic_Image_Inpainting_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Reduce_Information_Loss_in_Transformers_for_Pluralistic_Image_Inpainting_CVPR_2022_paper.html
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CVPR 2022
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Replacing Labeled Real-Image Datasets With Auto-Generated Contours
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Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota
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In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k without the use of real images, human-, and self-supervision during the pre-training of Vision Transformers (ViTs). For example, ViT-Base pre-trained on ImageNet-21k shows 81.8% top-1 accuracy when fine-tuned on ImageNet-1k and FDSL shows 82.7% top-1 accuracy when pre-trained under the same conditions (number of images, hyperparameters, and number of epochs). Images generated by formulas avoid the privacy/copyright issues, labeling cost and errors, and biases that real images suffer from, and thus have tremendous potential for pre-training general models. To understand the performance of the synthetic images, we tested two hypotheses, namely (i) object contours are what matter in FDSL datasets and (ii) increased number of parameters to create labels affects performance improvement in FDSL pre-training. To test the former hypothesis, we constructed a dataset that consisted of simple object contour combinations. We found that this dataset can match the performance of fractals. For the latter hypothesis, we found that increasing the difficulty of the pre-training task generally leads to better fine-tuning accuracy.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Kataoka_Replacing_Labeled_Real-Image_Datasets_With_Auto-Generated_Contours_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Kataoka_Replacing_Labeled_Real-Image_Datasets_With_Auto-Generated_Contours_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Kataoka_Replacing_Labeled_Real-Image_Datasets_With_Auto-Generated_Contours_CVPR_2022_paper.html
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CVPR 2022
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Cross-Modal Transferable Adversarial Attacks From Images to Videos
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Zhipeng Wei, Jingjing Chen, Zuxuan Wu, Yu-Gang Jiang
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Recent studies have shown that adversarial examples hand-crafted on one white box model can be used to attack other black-box models. Such cross-model transferability makes it feasible to perform black-box attacks, which has raised security concerns for real-world DNNs applications. Nevertheless, existing works mostly focus on investigating the adversarial transferability across different deep models that share the same modality of input data. The cross-modal transferability of adversarial perturbation has never been explored. This paper investigates the transferability of adversarial perturbation across different modalities, i.e., leveraging adversarial perturbation generated on white-box image models to attack black-box video models. Specifically, motivated by the observation that the low-level feature space between images and video frames are similar, we propose a simple yet effective cross-modal attack method, named as Image To Video (I2V) attack. I2V generates adversarial frames by minimizing the cosine similarity between features of pre-trained image models from adversarial and benign examples, then combines the generated adversarial frames to perform black-box attacks on video recognition models. Extensive experiments demonstrate that I2V can achieve high attack success rates on different black-box video recognition models. On Kinetics-400 and UCF-101, I2V achieves an average attack success rate of 77.88% and 65.68%, respectively, which sheds light on the feasibility of cross-modal adversarial attacks.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wei_Cross-Modal_Transferable_Adversarial_Attacks_From_Images_to_Videos_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wei_Cross-Modal_Transferable_Adversarial_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.05379
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Cross-Modal_Transferable_Adversarial_Attacks_From_Images_to_Videos_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Cross-Modal_Transferable_Adversarial_Attacks_From_Images_to_Videos_CVPR_2022_paper.html
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CVPR 2022
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Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection
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Jingqun Tang, Wenqing Zhang, Hongye Liu, MingKun Yang, Bo Jiang, Guanglong Hu, Xiang Bai
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Recently, transformer-based methods have achieved promising progresses in object detection, as they can eliminate the post-processes like NMS and enrich the deep representations. However, these methods cannot well cope with scene text due to its extreme variance of scales and aspect ratios. In this paper, we present a simple yet effective transformer-based architecture for scene text detection. Different from previous approaches that learn robust deep representations of scene text in a holistic manner, our method performs scene text detection based on a few representative features, which avoids the disturbance by background and reduces the computational cost. Specifically, we first select a few representative features at all scales that are highly relevant to foreground text. Then, we adopt a transformer for modeling the relationship of the sampled features, which effectively divides them into reasonable groups. As each feature group corresponds to a text instance, its bounding box can be easily obtained without any post-processing operation. Using the basic feature pyramid network for feature extraction, our method consistently achieves state-of-the-art results on several popular datasets for scene text detection.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Few_Could_Be_Better_Than_All_Feature_Sampling_and_Grouping_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Few_Could_Be_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.15221
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Few_Could_Be_Better_Than_All_Feature_Sampling_and_Grouping_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Few_Could_Be_Better_Than_All_Feature_Sampling_and_Grouping_CVPR_2022_paper.html
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CVPR 2022
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Do Explanations Explain? Model Knows Best
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Ashkan Khakzar, Pedram Khorsandi, Rozhin Nobahari, Nassir Navab
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It is a mystery which input features contribute to a neural network's output. Various explanations methods are proposed in the literature to shed light on the problem. One peculiar observation is that these explanations point to different features as being important. The phenomenon raises the question, which explanation to trust? We propose a framework for evaluating the explanations using the neural network model itself. The framework leverages the network to generate input features that impose a particular behavior on the output. Using the generated features, we devise controlled experimental setups to evaluate whether an explanation method conforms to an axiom. Thus we propose an empirical framework for axiomatic evaluation of explanation methods. We evaluate well-known and promising explanation solutions using the proposed framework. The framework provides a toolset to reveal properties and drawbacks within existing and future explanation solutions.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Khakzar_Do_Explanations_Explain_Model_Knows_Best_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Khakzar_Do_Explanations_Explain_CVPR_2022_supplemental.zip
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http://arxiv.org/abs/2203.02269
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Khakzar_Do_Explanations_Explain_Model_Knows_Best_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Khakzar_Do_Explanations_Explain_Model_Knows_Best_CVPR_2022_paper.html
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CVPR 2022
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WebQA: Multihop and Multimodal QA
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Yingshan Chang, Mridu Narang, Hisami Suzuki, Guihong Cao, Jianfeng Gao, Yonatan Bisk
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Scaling Visual Question Answering (VQA) to the open-domain and multi-hop nature of web searches, requires fundamental advances in visual representation learning, knowledge aggregation, and language generation. In this work, we introduce WebQA, a challenging new benchmark that proves difficult for large-scale state-of-the-art models which lack language groundable visual representations for novel objects and the ability to reason, yet trivial for humans. WebQA mirrors the way humans use the web: 1) Ask a question, 2) Choose sources to aggregate, and 3) Produce a fluent language response. This is the behavior we should be expecting from IoT devices and digital assistants. Existing work prefers to assume that a model can either reason about knowledge in images or in text. WebQA includes a secondary text-only QA task to ensure improved visual performance does not come at the cost of language understanding. Our challenge for the community is to create unified multimodal reasoning models that answer questions regardless of the source modality, moving us closer to digital assistants that not only query language knowledge, but also the richer visual online world.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Chang_WebQA_Multihop_and_Multimodal_QA_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chang_WebQA_Multihop_and_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2109.00590
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Chang_WebQA_Multihop_and_Multimodal_QA_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Chang_WebQA_Multihop_and_Multimodal_QA_CVPR_2022_paper.html
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CVPR 2022
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Occlusion-Robust Face Alignment Using a Viewpoint-Invariant Hierarchical Network Architecture
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Congcong Zhu, Xintong Wan, Shaorong Xie, Xiaoqiang Li, Yinzheng Gu
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The occlusion problem heavily degrades the localization performance of face alignment. Most current solutions for this problem focus on annotating new occlusion data, introducing boundary estimation, and stacking deeper models to improve the robustness of neural networks. However, the performance degradation of models remains under extreme occlusion (average occlusion of over 50%) because of missing a large amount of facial context information. We argue that exploring neural networks to model the facial hierarchies is a more promising method for dealing with extreme occlusion. Surprisingly, in recent studies, little effort has been devoted to representing the facial hierarchies using neural networks. This paper proposes a new network architecture called GlomFace to model the facial hierarchies against various occlusions, which draws inspiration from the viewpoint-invariant hierarchy of facial structure. Specifically, GlomFace is functionally divided into two modules: the part-whole hierarchical module and the whole-part hierarchical module. The former captures the part-whole hierarchical dependencies of facial parts to suppress multi-scale occlusion information, whereas the latter injects structural reasoning into neural networks by building the whole-part hierarchical relations among facial parts. As a result, GlomFace has a clear topological interpretation due to its correspondence to the facial hierarchies. Extensive experimental results indicate that the proposed GlomFace performs comparably to existing state-of-the-art methods, especially in cases of extreme occlusion. Models are available at https://github.com/zhuccly/GlomFace-Face-Alignment.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Occlusion-Robust_Face_Alignment_Using_a_Viewpoint-Invariant_Hierarchical_Network_Architecture_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Occlusion-Robust_Face_Alignment_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Occlusion-Robust_Face_Alignment_Using_a_Viewpoint-Invariant_Hierarchical_Network_Architecture_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Occlusion-Robust_Face_Alignment_Using_a_Viewpoint-Invariant_Hierarchical_Network_Architecture_CVPR_2022_paper.html
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CVPR 2022
| null |
BasicVSR++: Improving Video Super-Resolution With Enhanced Propagation and Alignment
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Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy
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A recurrent structure is a popular framework choice for the task of video super-resolution. The state-of-the-art method BasicVSR adopts bidirectional propagation with feature alignment to effectively exploit information from the entire input video. In this study, we redesign BasicVSR by proposing second-order grid propagation and flow-guided deformable alignment. We show that by empowering the recurrent framework with enhanced propagation and alignment, one can exploit spatiotemporal information across misaligned video frames more effectively. The new components lead to an improved performance under a similar computational constraint. In particular, our model BasicVSR++ surpasses BasicVSR by a significant 0.82 dB in PSNR with similar number of parameters. BasicVSR++ is generalizable to other video restoration tasks, and obtains three champions and one first runner-up in NTIRE 2021 video restoration challenge.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Chan_BasicVSR_Improving_Video_Super-Resolution_With_Enhanced_Propagation_and_Alignment_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chan_BasicVSR_Improving_Video_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Chan_BasicVSR_Improving_Video_Super-Resolution_With_Enhanced_Propagation_and_Alignment_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Chan_BasicVSR_Improving_Video_Super-Resolution_With_Enhanced_Propagation_and_Alignment_CVPR_2022_paper.html
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CVPR 2022
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IDR: Self-Supervised Image Denoising via Iterative Data Refinement
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Yi Zhang, Dasong Li, Ka Lung Law, Xiaogang Wang, Hongwei Qin, Hongsheng Li
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The lack of large-scale noisy-clean image pairs restricts supervised denoising methods' deployment in actual applications. While existing unsupervised methods are able to learn image denoising without ground-truth clean images, they either show poor performance or work under impractical settings (e.g., paired noisy images). In this paper, we present a practical unsupervised image denoising method to achieve state-of-the-art denoising performance. Our method only requires single noisy images and a noise model, which is easily accessible in practical raw image denoising. It performs two steps iteratively: (1) Constructing a noisier-noisy dataset with random noise from the noise model; (2) training a model on the noisier-noisy dataset and using the trained model to refine noisy images to obtain the targets used in the next round. We further approximate our full iterative method with a fast algorithm for more efficient training while keeping its original high performance. Experiments on real-world, synthetic, and correlated noise show that our proposed unsupervised denoising approach has superior performances over existing unsupervised methods and competitive performance with supervised methods. In addition, we argue that existing denoising datasets are of low quality and contain only a small number of scenes. To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes. The dataset can serve as a strong benchmark for better evaluating raw image denoising. Code and dataset will be released at https://github.com/zhangyi-3/IDR
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_IDR_Self-Supervised_Image_Denoising_via_Iterative_Data_Refinement_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_IDR_Self-Supervised_Image_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2111.14358
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_IDR_Self-Supervised_Image_Denoising_via_Iterative_Data_Refinement_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_IDR_Self-Supervised_Image_Denoising_via_Iterative_Data_Refinement_CVPR_2022_paper.html
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CVPR 2022
| null |
MogFace: Towards a Deeper Appreciation on Face Detection
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Yang Liu, Fei Wang, Jiankang Deng, Zhipeng Zhou, Baigui Sun, Hao Li
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Benefiting from the pioneering design of generic object detectors, significant achievements have been made in the field of face detection. Typically, the architectures of the backbone, feature pyramid layer, and detection head module within the face detector all assimilate the excellent experience from general object detectors. However, several effective methods, including label assignment and scale-level data augmentation strategy, fail to maintain consistent superiority when applying on the face detector directly. Concretely, the former strategy involves a vast body of hyper-parameters and the latter one suffers from the challenge of scale distribution bias between different detection tasks, which both limit their generalization abilities. Furthermore, in order to provide accurate face bounding boxes for facial down-stream tasks, the face detector imperatively requires the elimination of false alarms. As a result, practical solutions on label assignment, scale-level data augmentation, and reducing false alarms are necessary for advancing face detectors. In this paper, we focus on resolving three aforementioned challenges that exiting methods are difficult to finish off and present a novel face detector, termed MogFace. In our Mogface, three key components, Adaptive Online Incremental Anchor Mining Strategy, Selective Scale Enhancement Strategy and Hierarchical Context-Aware Module, are separately proposed to boost the performance of face detectors. Finally, to the best of our knowledge, our MogFace is the best face detector on the Wider Face leader-board, achieving all champions across different testing scenarios. The code is available at https://github.com/damo-cv/MogFace.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_MogFace_Towards_a_Deeper_Appreciation_on_Face_Detection_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_MogFace_Towards_a_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2103.11139
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Liu_MogFace_Towards_a_Deeper_Appreciation_on_Face_Detection_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Liu_MogFace_Towards_a_Deeper_Appreciation_on_Face_Detection_CVPR_2022_paper.html
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CVPR 2022
| null |
GuideFormer: Transformers for Image Guided Depth Completion
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Kyeongha Rho, Jinsung Ha, Youngjung Kim
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Depth completion has been widely studied to predict a dense depth image from its sparse measurement and a single color image. However, most state-of-the-art methods rely on static convolutional neural networks (CNNs) which are not flexible enough for capturing the dynamic nature of input contexts. In this paper, we propose GuideFormer, a fully transformer-based architecture for dense depth completion. We first process sparse depth and color guidance images with separate transformer branches to extract hierarchical and complementary token representations. Each branch consists of a stack of self-attention blocks and has key design features to make our model suitable for the task. We also devise an effective token fusion method based on guided-attention mechanism. It explicitly models information flow between the two branches and captures inter-modal dependencies that cannot be obtained from depth or color image alone. These properties allow GuideFormer to enjoy various visual dependencies and recover precise depth values while preserving fine details. We evaluate GuideFormer on the KITTI dataset containing real-world driving scenes and provide extensive ablation studies. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art methods.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Rho_GuideFormer_Transformers_for_Image_Guided_Depth_Completion_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rho_GuideFormer_Transformers_for_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Rho_GuideFormer_Transformers_for_Image_Guided_Depth_Completion_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Rho_GuideFormer_Transformers_for_Image_Guided_Depth_Completion_CVPR_2022_paper.html
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CVPR 2022
| null |
Multi-Label Iterated Learning for Image Classification With Label Ambiguity
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Sai Rajeswar, Pau Rodríguez, Soumye Singhal, David Vazquez, Aaron Courville
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Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly labeled since images with multiple object classes present are assigned a single label. This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data. Inspired by language emergence literature, we propose multi-label iterated learning (MILe) to incorporate the inductive biases of multi-label learning from single labels using the framework of iterated learning. MILe is a simple yet effective procedure that builds a multi-label description of the image by propagating binary predictions through successive generations of teacher and student networks with a learning bottleneck. Experiments show that our approach exhibits systematic benefits on ImageNet accuracy as well as ReaL F1 score, which indicates that MILe deals better with label ambiguity than the standard training procedure, even when fine-tuning from self-supervised weights. We also show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision. Furthermore, MILe improves performance in class incremental settings such as IIRC and it is robust to distribution shifts.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Rajeswar_Multi-Label_Iterated_Learning_for_Image_Classification_With_Label_Ambiguity_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rajeswar_Multi-Label_Iterated_Learning_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Rajeswar_Multi-Label_Iterated_Learning_for_Image_Classification_With_Label_Ambiguity_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Rajeswar_Multi-Label_Iterated_Learning_for_Image_Classification_With_Label_Ambiguity_CVPR_2022_paper.html
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CVPR 2022
| null |
Region-Aware Face Swapping
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Chao Xu, Jiangning Zhang, Miao Hua, Qian He, Zili Yi, Yong Liu
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This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner: 1) Local Facial Region-Aware (FRA) branch augments local identity-relevant features by introducing the Transformer to effectively model misaligned cross-scale semantic interaction. 2) Global Source Feature-Adaptive (SFA) branch further complements global identity-relevant cues for generating identity-consistent swapped faces. Besides, we propose a Face Mask Predictor (FMP) module incorporated with StyleGAN2 to predict identity-relevant soft facial masks in an unsupervised manner that is more practical for generating harmonious high-resolution faces. Abundant experiments qualitatively and quantitatively demonstrate the superiority of our method for generating more identity-consistent high-resolution swapped faces over SOTA methods, e.g., obtaining 96.70 ID retrieval that outperforms SOTA MegaFS by 5.87.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Region-Aware_Face_Swapping_CVPR_2022_paper.pdf
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http://arxiv.org/abs/2203.04564
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Region-Aware_Face_Swapping_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Region-Aware_Face_Swapping_CVPR_2022_paper.html
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CVPR 2022
| null |
Towards Language-Free Training for Text-to-Image Generation
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Yufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, Tong Sun
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One of the major challenges in training text-to-image generation models is the need of a large number of high-quality text-image pairs. While image samples are often easily accessible, the associated text description typically requires careful human captioning, which is particularly time- and cost-consuming. In this paper, we propose the first work to train text-to-image generation models without any text data. It intelligently leverages the well-aligned cross-modal semantic space of the powerful pre-trained CLIP model: the requirement of text-conditioning is alleviated via generating text features from image features. Extensive experiments are conducted to illustrate the effectiveness of the proposed method. We obtain state-of-the-art results in the standard text-to-image generation tasks. Importantly, the proposed language-free model outperforms most existing models trained with full text-image pairs. Furthermore, our method can be applied in fine-tuning pre-trained models, which saves both training time and cost in training text-to-image generation models. Our pre-trained model obtains competitive results in zero-shot text-to-image generation on MS-COCO dataset, yet with around only 1% of the model size compared to the recently proposed large DALL-E model.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Towards_Language-Free_Training_for_Text-to-Image_Generation_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Towards_Language-Free_Training_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2111.13792
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Towards_Language-Free_Training_for_Text-to-Image_Generation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Towards_Language-Free_Training_for_Text-to-Image_Generation_CVPR_2022_paper.html
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CVPR 2022
| null |
Learning Affinity From Attention: End-to-End Weakly-Supervised Semantic Segmentation With Transformers
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Lixiang Ru, Yibing Zhan, Baosheng Yu, Bo Du
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Weakly-supervised semantic segmentation (WSSS) with image-level labels is an important and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS have received increasing attention from the community. However, current methods are mainly based on convolutional neural networks and fail to explore the global information properly, thus usually resulting in incomplete object regions. In this paper, to address the aforementioned problem, we introduce Transformers, which naturally integrate global information, to generate more integral initial pseudo labels for end-to-end WSSS. Motivated by the inherent consistency between the self-attention in Transformers and the semantic affinity, we propose an Affinity from Attention (AFA) module to learn semantic affinity from the multi-head self-attention (MHSA) in Transformers. The learned affinity is then leveraged to refine the initial pseudo labels for segmentation. In addition, to efficiently derive reliable affinity labels for supervising AFA and ensure the local consistency of pseudo labels, we devise a Pixel-Adaptive Refinement module that incorporates low-level image appearance information to refine the pseudo labels. We perform extensive experiments and our method achieves 66.0% and 38.9% mIoU on the PASCAL VOC 2012 and MS COCO 2014 datasets, respectively, significantly outperforming recent end-to-end methods and several multi-stage competitors. Code is available at https://github.com/rulixiang/afa.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Ru_Learning_Affinity_From_Attention_End-to-End_Weakly-Supervised_Semantic_Segmentation_With_Transformers_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ru_Learning_Affinity_From_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.02664
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Ru_Learning_Affinity_From_Attention_End-to-End_Weakly-Supervised_Semantic_Segmentation_With_Transformers_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Ru_Learning_Affinity_From_Attention_End-to-End_Weakly-Supervised_Semantic_Segmentation_With_Transformers_CVPR_2022_paper.html
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CVPR 2022
| null |
Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees
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Magzhan Gabidolla, Miguel Á. Carreira-Perpiñán
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Ensemble methods based on decision trees, such as Random Forests or boosted forests, have long been established as some of the most powerful, off-the-shelf machine learning models, and have been widely used in computer vision and other areas. In recent years, a specific form of boosting, gradient boosting (GB), has gained prominence. This is partly because of highly optimized implementations such as XGBoost or LightGBM, which incorporate many clever modifications and heuristics. However, one gaping hole remains unexplored in GB: the construction of individual trees. To date, all successful GB versions use axis-aligned trees trained in a suboptimal way via greedy recursive partitioning. We address this gap by using a more powerful type of trees (having hyperplane splits) and an algorithm that can optimize, globally over all the tree parameters, the objective function that GB dictates. We show, in several benchmarks of image and other data types, that GB forests of these stronger, well-optimized trees consistently exceed the test accuracy of axis-aligned forests from XGBoost, LightGBM and other strong baselines. Further, this happens using many fewer trees and sometimes even fewer parameters overall.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Gabidolla_Pushing_the_Envelope_of_Gradient_Boosting_Forests_via_Globally-Optimized_Oblique_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gabidolla_Pushing_the_Envelope_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Gabidolla_Pushing_the_Envelope_of_Gradient_Boosting_Forests_via_Globally-Optimized_Oblique_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Gabidolla_Pushing_the_Envelope_of_Gradient_Boosting_Forests_via_Globally-Optimized_Oblique_CVPR_2022_paper.html
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CVPR 2022
| null |
Physical Simulation Layer for Accurate 3D Modeling
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Mariem Mezghanni, Théo Bodrito, Malika Boulkenafed, Maks Ovsjanikov
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We introduce a novel approach for generative 3D modeling that explicitly encourages the physical and thus functional consistency of the generated shapes. To this end, we advocate the use of online physical simulation as part of learning a generative model. Unlike previous related methods, our approach is trained end-to-end with a fully differentiable physical simulator in the training loop. We accomplish this by leveraging recent advances in differentiable programming, and introducing a fully differentiable point-based physical simulation layer, which accurately evaluates the shape's stability when subjected to gravity. We then incorporate this layer in a signed distance function (SDF) shape decoder. By augmenting a conventional SDF decoder with our simulation layer, we demonstrate through extensive experiments that online physical simulation improves the accuracy, visual plausibility and physical validity of the resulting shapes, while requiring no additional data or annotation effort.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Mezghanni_Physical_Simulation_Layer_for_Accurate_3D_Modeling_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mezghanni_Physical_Simulation_Layer_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Mezghanni_Physical_Simulation_Layer_for_Accurate_3D_Modeling_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Mezghanni_Physical_Simulation_Layer_for_Accurate_3D_Modeling_CVPR_2022_paper.html
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CVPR 2022
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Deformable Sprites for Unsupervised Video Decomposition
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Vickie Ye, Zhengqi Li, Richard Tucker, Angjoo Kanazawa, Noah Snavely
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We describe a method to extract persistent elements of a dynamic scene from an input video. We represent each scene element as a Deformable Sprite consisting of three components: 1) a 2D texture image for the entire video, 2) per-frame masks for the element, and 3) non-rigid deformations that map the texture image into each video frame. The resulting decomposition allows for applications such as consistent video editing. Deformable Sprites are a type of video auto-encoder model that is optimized on individual videos, and does not require training on a large dataset, nor does it rely on pre-trained models. Moreover, our method does not require object masks or other user input, and discovers moving objects of a wider variety than previous work. We evaluate our approach on standard video datasets and show qualitative results on a diverse array of Internet videos.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_Deformable_Sprites_for_Unsupervised_Video_Decomposition_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_Deformable_Sprites_for_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2204.07151
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Deformable_Sprites_for_Unsupervised_Video_Decomposition_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Deformable_Sprites_for_Unsupervised_Video_Decomposition_CVPR_2022_paper.html
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CVPR 2022
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CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation
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Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, Wenjie Li, Lijun Chen
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In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data. Previous methods either employ a complex pipeline that splits the joint task into independent stages, or fuse 2D and 3D information in an "early-fusion" or "late-fusion" manner. Such one-size-fits-all approaches suffer from a dilemma of failing to fully utilize the characteristic of each modality or to maximize the inter-modality complementarity. To address the problem, we propose a novel end-to-end framework, called CamLiFlow. It consists of 2D and 3D branches with multiple bidirectional connections between them in specific layers. Different from previous work, we apply a point-based 3D branch to better extract the geometric features and design a symmetric learnable operator to fuse dense image features and sparse point features. Experiments show that CamLiFlow achieves better performance with fewer parameters. Our method ranks 1st on the KITTI Scene Flow benchmark, outperforming the previous art with 1/7 parameters. Code is available at https://github.com/MCG-NJU/CamLiFlow.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_CamLiFlow_Bidirectional_Camera-LiDAR_Fusion_for_Joint_Optical_Flow_and_Scene_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_CamLiFlow_Bidirectional_Camera-LiDAR_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2111.10502
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Liu_CamLiFlow_Bidirectional_Camera-LiDAR_Fusion_for_Joint_Optical_Flow_and_Scene_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Liu_CamLiFlow_Bidirectional_Camera-LiDAR_Fusion_for_Joint_Optical_Flow_and_Scene_CVPR_2022_paper.html
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CVPR 2022
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FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos
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Yan Wang, Yixuan Sun, Yiwen Huang, Zhongying Liu, Shuyong Gao, Wei Zhang, Weifeng Ge, Wenqiang Zhang
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Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether performances of existing methods remain satisfactory in real-world application-oriented scenes. For example, the "Happy" expression with high intensity in Talk-Show is more discriminating than the same expression with low intensity in Official-Event. To fill this gap, we build a large-scale multi-scene dataset, coined as FERV39k. We analyze the important ingredients of constructing such a novel dataset in three aspects: (1) multi-scene hierarchy and expression class, (2) generation of candidate video clips, (3) trusted manual labelling process. Based on these guidelines, we select 4 scenarios subdivided into 22 scenes, annotate 86k samples automatically obtained from 4k videos based on the well-designed workflow, and finally build 38,935 video clips labeled with 7 classic expressions. Experiment benchmarks on four kinds of baseline frameworks were also provided and further analysis on their performance across different scenes and some challenges for future research were given. Besides, we systematically investigate key components of DFER by ablation studies. The baseline framework and our project are available on https://github.com/wangyanckxx/FERV39k.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_FERV39k_A_Large-Scale_Multi-Scene_Dataset_for_Facial_Expression_Recognition_in_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_FERV39k_A_Large-Scale_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.09463
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_FERV39k_A_Large-Scale_Multi-Scene_Dataset_for_Facial_Expression_Recognition_in_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_FERV39k_A_Large-Scale_Multi-Scene_Dataset_for_Facial_Expression_Recognition_in_CVPR_2022_paper.html
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CVPR 2022
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Learning To Detect Mobile Objects From LiDAR Scans Without Labels
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Yurong You, Katie Luo, Cheng Perng Phoo, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
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Current 3D object detectors for autonomous driving are almost entirely trained on human-annotated data. Although of high quality, the generation of such data is laborious and costly, restricting them to a few specific locations and object types. This paper proposes an alternative approach entirely based on unlabeled data, which can be collected cheaply and in abundance almost everywhere on earth. Our approach leverages several simple common sense heuristics to create an initial set of approximate seed labels. For example, relevant traffic participants are generally not persistent across multiple traversals of the same route, do not fly, and are never under ground. We demonstrate that these seed labels are highly effective to bootstrap a surprisingly accurate detector through repeated self-training without a single human annotated label. Code is available at https://github.com/YurongYou/MODEST.
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https://openaccess.thecvf.com/content/CVPR2022/papers/You_Learning_To_Detect_Mobile_Objects_From_LiDAR_Scans_Without_Labels_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/You_Learning_To_Detect_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.15882
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/You_Learning_To_Detect_Mobile_Objects_From_LiDAR_Scans_Without_Labels_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/You_Learning_To_Detect_Mobile_Objects_From_LiDAR_Scans_Without_Labels_CVPR_2022_paper.html
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CVPR 2022
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BNV-Fusion: Dense 3D Reconstruction Using Bi-Level Neural Volume Fusion
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Kejie Li, Yansong Tang, Victor Adrian Prisacariu, Philip H.S. Torr
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Dense 3D reconstruction from a stream of depth images is the key to many mixed reality and robotic applications. Although methods based on Truncated Signed Distance Function (TSDF) Fusion have advanced the field over the years, the TSDF volume representation is confronted with striking a balance between the robustness to noisy measurements and maintaining the level of detail. We present Bi-level Neural Volume Fusion (BNV-Fusion), which leverages recent advances in neural implicit representations and neural rendering for dense 3D reconstruction. In order to incrementally integrate new depth maps into a global neural implicit representation, we propose a novel bi-level fusion strategy that considers both efficiency and reconstruction quality by design. We evaluate the proposed method on multiple datasets quantitatively and qualitatively, demonstrating a significant improvement over existing methods.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Li_BNV-Fusion_Dense_3D_Reconstruction_Using_Bi-Level_Neural_Volume_Fusion_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_BNV-Fusion_Dense_3D_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_BNV-Fusion_Dense_3D_Reconstruction_Using_Bi-Level_Neural_Volume_Fusion_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_BNV-Fusion_Dense_3D_Reconstruction_Using_Bi-Level_Neural_Volume_Fusion_CVPR_2022_paper.html
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CVPR 2022
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Probabilistic Representations for Video Contrastive Learning
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Jungin Park, Jiyoung Lee, Ig-Jae Kim, Kwanghoon Sohn
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This paper presents Probabilistic Video Contrastive Learning, a self-supervised representation learning method that bridges contrastive learning with probabilistic representation. We hypothesize that the clips composing the video have different distributions in short-term duration, but can represent the complicated and sophisticated video distribution through combination in a common embedding space. Thus, the proposed method represents video clips as normal distributions and combines them into a Mixture of Gaussians to model the whole video distribution. By sampling embeddings from the whole video distribution, we can circumvent the careful sampling strategy or transformations to generate augmented views of the clips, unlike previous deterministic methods that have mainly focused on such sample generation strategies for contrastive learning. We further propose a stochastic contrastive loss to learn proper video distributions and handle the inherent uncertainty from the nature of the raw video. Experimental results verify that our probabilistic embedding stands as a state-of-the-art video representation learning for action recognition and video retrieval on the most popular benchmarks, including UCF101 and HMDB51.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Probabilistic_Representations_for_Video_Contrastive_Learning_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Probabilistic_Representations_for_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2204.03946
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Park_Probabilistic_Representations_for_Video_Contrastive_Learning_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Park_Probabilistic_Representations_for_Video_Contrastive_Learning_CVPR_2022_paper.html
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CVPR 2022
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EnvEdit: Environment Editing for Vision-and-Language Navigation
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Jialu Li, Hao Tan, Mohit Bansal
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In Vision-and-Language Navigation (VLN), an agent needs to navigate through the environment based on natural language instructions. Due to limited available data for agent training and finite diversity in navigation environments, it is challenging for the agent to generalize to new, unseen environments. To address this problem, we propose EnvEdit, a data augmentation method that creates new environments by editing existing environments, which are used to train a more generalizable agent. Our augmented environments can differ from the seen environments in three diverse aspects: style, object appearance, and object classes. Training on these edit-augmented environments prevents the agent from overfitting to existing environments and helps generalize better to new, unseen environments. Empirically, on both the Room-to-Room and the multi-lingual Room-Across-Room datasets, we show that our proposed EnvEdit method gets significant improvements in all metrics on both pre-trained and non-pre-trained VLN agents, and achieves the new state-of-the-art on the test leaderboard. We further ensemble the VLN agents augmented on different edited environments and show that these edit methods are complementary.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Li_EnvEdit_Environment_Editing_for_Vision-and-Language_Navigation_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_EnvEdit_Environment_Editing_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.15685
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_EnvEdit_Environment_Editing_for_Vision-and-Language_Navigation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_EnvEdit_Environment_Editing_for_Vision-and-Language_Navigation_CVPR_2022_paper.html
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CVPR 2022
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Omnivore: A Single Model for Many Visual Modalities
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Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, Ishan Misra
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Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data. Instead, in this paper, we propose a single model which excels at classifying images, videos, and single-view 3D data using exactly the same model parameters. Our 'OMNIVORE' model leverages the flexibility of transformer-based architectures and is trained jointly on classification tasks from different modalities. OMNIVORE is simple to train, uses off-the-shelf standard datasets, and performs at-par or better than modality-specific models of the same size. A single OMNIVORE model obtains 86.0% on ImageNet, 84.1% on Kinetics, and 67.1% on SUN RGB-D. After finetuning, our models outperform prior work on a variety of vision tasks and generalize across modalities. OMNIVORE's shared visual representation naturally enables cross-modal recognition without access to correspondences between modalities. We hope our results motivate researchers to model visual modalities together.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Girdhar_Omnivore_A_Single_Model_for_Many_Visual_Modalities_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Girdhar_Omnivore_A_Single_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2201.08377
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Girdhar_Omnivore_A_Single_Model_for_Many_Visual_Modalities_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Girdhar_Omnivore_A_Single_Model_for_Many_Visual_Modalities_CVPR_2022_paper.html
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CVPR 2022
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Neural Shape Mating: Self-Supervised Object Assembly With Adversarial Shape Priors
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Yun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, Animesh Garg
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Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally: as mating geometric parts together to achieve a snug fit. By focusing on shape alignment rather than semantic cues, we can achieve across category generalization and scaling. In this paper, we introduce a novel task, pairwise 3D geometric shape mating, and propose Neural Shape Mating (NSM) to tackle this problem. Given point clouds of two object parts of an unknown category, NSM learns to reason about the fit of the two parts and predict a pair of 3D poses that tightly mate them together. In addition, we couple the training of NSM with an implicit shape reconstruction task, making NSM more robust to imperfect point cloud observations. To train NSM, we present a self-supervised data collection pipeline that generates pairwise shape mating data with ground truth by randomly cutting an object mesh into two parts, resulting in a dataset that consists of 200K shape mating pairs with numerous object meshes and diverse cut types. We train NSM on the collected dataset and compare it with several point cloud registration methods and one part assembly baseline approach. Extensive experimental results and ablation studies under various settings demonstrate the effectiveness of the proposed algorithm. Additional material is available at: neural-shape-mating.github.io.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Neural_Shape_Mating_Self-Supervised_Object_Assembly_With_Adversarial_Shape_Priors_CVPR_2022_paper.pdf
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http://arxiv.org/abs/2205.14886
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Neural_Shape_Mating_Self-Supervised_Object_Assembly_With_Adversarial_Shape_Priors_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Neural_Shape_Mating_Self-Supervised_Object_Assembly_With_Adversarial_Shape_Priors_CVPR_2022_paper.html
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CVPR 2022
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Reflash Dropout in Image Super-Resolution
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Xiangtao Kong, Xina Liu, Jinjin Gu, Yu Qiao, Chao Dong
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Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a different behaviour as high-level tasks and is sensitive to the dropout operation. However, in this paper, we show that appropriate usage of dropout benefits SR networks and improves the generalization ability. Specifically, dropout is better embedded at the end of the network and is significantly helpful for the multi-degradation settings. This discovery breaks our common sense and inspires us to explore its working mechanism. We further use two analysis tools -- one is from recent network interpretation works, and the other is specially designed for this task. The analysis results provide side proofs to our experimental findings and show us a new perspective to understand SR networks.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Kong_Reflash_Dropout_in_Image_Super-Resolution_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kong_Reflash_Dropout_in_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.12089
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Reflash_Dropout_in_Image_Super-Resolution_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Reflash_Dropout_in_Image_Super-Resolution_CVPR_2022_paper.html
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CVPR 2022
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WildNet: Learning Domain Generalized Semantic Segmentation From the Wild
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Suhyeon Lee, Hongje Seong, Seongwon Lee, Euntai Kim
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We present a new domain generalized semantic segmentation network named WildNet, which learns domain-generalized features by leveraging a variety of contents and styles from the wild. In domain generalization, the low generalization ability for unseen target domains is clearly due to overfitting to the source domain. To address this problem, previous works have focused on generalizing the domain by removing or diversifying the styles of the source domain. These alleviated overfitting to the source-style but overlooked overfitting to the source-content. In this paper, we propose to diversify both the content and style of the source domain with the help of the wild. Our main idea is for networks to naturally learn domain-generalized semantic information from the wild. To this end, we diversify styles by augmenting source features to resemble wild styles and enable networks to adapt to a variety of styles. Furthermore, we encourage networks to learn class-discriminant features by providing semantic variations borrowed from the wild to source contents in the feature space. Finally, we regularize networks to capture consistent semantic information even when both the content and style of the source domain are extended to the wild. Extensive experiments on five different datasets validate the effectiveness of our WildNet, and we significantly outperform state-of-the-art methods. The source code and model are available online: https://github.com/suhyeonlee/WildNet.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_WildNet_Learning_Domain_Generalized_Semantic_Segmentation_From_the_Wild_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_WildNet_Learning_Domain_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2204.01446
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Lee_WildNet_Learning_Domain_Generalized_Semantic_Segmentation_From_the_Wild_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Lee_WildNet_Learning_Domain_Generalized_Semantic_Segmentation_From_the_Wild_CVPR_2022_paper.html
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CVPR 2022
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Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
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Zhuohang Li, Jiaxin Zhang, Luyang Liu, Jian Liu
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Federated Learning (FL) framework brings privacy benefits to distributed learning systems by allowing multiple clients to participate in a learning task under the coordination of a central server without exchanging their private data. However, recent studies have revealed that private information can still be leaked through shared gradient information. To further protect user's privacy, several defense mechanisms have been proposed to prevent privacy leakage via gradient information degradation methods, such as using additive noise or gradient compression before sharing it with the server. In this work, we validate that the private training data can still be leaked under certain defense settings with a new type of leakage, i.e., Generative Gradient Leakage (GGL). Unlike existing methods that only rely on gradient information to reconstruct data, our method leverages the latent space of generative adversarial networks (GAN) learned from public image datasets as a prior to compensate for the informational loss during gradient degradation. To address the nonlinearity caused by the gradient operator and the GAN model, we explore various gradient-free optimization methods (e.g., evolution strategies and Bayesian optimization) and empirically show their superiority in reconstructing high-quality images from gradients compared to gradient-based optimizers. We hope the proposed method can serve as a tool for empirically measuring the amount of privacy leakage to facilitate the design of more robust defense mechanisms.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Auditing_Privacy_Defenses_in_Federated_Learning_via_Generative_Gradient_Leakage_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Auditing_Privacy_Defenses_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.15696
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_Auditing_Privacy_Defenses_in_Federated_Learning_via_Generative_Gradient_Leakage_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_Auditing_Privacy_Defenses_in_Federated_Learning_via_Generative_Gradient_Leakage_CVPR_2022_paper.html
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CVPR 2022
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DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
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Haibao Yu, Yizhen Luo, Mao Shu, Yiyi Huo, Zebang Yang, Yifeng Shi, Zhenglong Guo, Hanyu Li, Xing Hu, Jirui Yuan, Zaiqing Nie
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Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities. It has been widely agreed that vehicle-infrastructure cooperation is required to achieve Level 5 autonomy. However, there is still NO dataset from real scenarios available for computer vision researchers to work on vehicle-infrastructure cooperation-related problems. To accelerate computer vision research and innovation for Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD), we release DAIR-V2X Dataset, which is the first large-scale, multi-modal, multi-view dataset from real scenarios for VICAD. DAIR-V2X comprises 71254 LiDAR frames and 71254 Camera frames, and all frames are captured from real scenes with 3D annotations. The Vehicle-Infrastructure Cooperative 3D Object Detection problem (VIC3D) is introduced, formulating the problem of collaboratively locating and identifying 3D objects using sensory input from both vehicles and infrastructure. In addition to solving traditional 3D object detection problems, the solution of VIC3D needs to consider the time asynchrony problem between vehicle and infrastructure sensors and the data transmission cost between them. Furthermore, we propose Time Compensation Late Fusion (TCLF), a late fusion framework for the VIC3D task as a benchmark based on DAIR-V2X. Find data, code, and more up-to-date information at \href https://thudair.baai.ac.cn/index https://thudair.baai.ac.cn/index and \href https://github.com/AIR-THU/DAIR-V2X https://github.com/AIR-THU/DAIR-V2X .
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https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_DAIR-V2X_A_Large-Scale_Dataset_for_Vehicle-Infrastructure_Cooperative_3D_Object_Detection_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Yu_DAIR-V2X_A_Large-Scale_Dataset_for_Vehicle-Infrastructure_Cooperative_3D_Object_Detection_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Yu_DAIR-V2X_A_Large-Scale_Dataset_for_Vehicle-Infrastructure_Cooperative_3D_Object_Detection_CVPR_2022_paper.html
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CVPR 2022
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DECORE: Deep Compression With Reinforcement Learning
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Manoj Alwani, Yang Wang, Vashisht Madhavan
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Deep learning has become an increasingly popular and powerful methodology for modern pattern recognition systems. However, many deep neural networks have millions or billions of parameters, making them untenable for real-world applications due to constraints on memory size or latency requirements. As a result, efficient network compression techniques are often required for a widespread adoption of deep learning methods. We present DECORE, a reinforcement learning based approach to automate the network compression process. DECORE assigns an agent to each channel in the network along with a light policy gradient method to learn which neurons or channels to be kept or removed. Each agent in the network has just one parameter (keep or drop) to learn, which leads to a much faster training process compared to existing approaches. DECORE also gives state-of-the-art compression results on various network architectures and various datasets. For example, on the ResNet-110 architecture, DECORE achieves a 64.8% compression rate and 61.8% FLOPs reduction as compared to the baseline model without any major accuracy loss on the CIFAR-10 dataset. It can reduce the size of regular architectures like the VGG network by up to 99% with just a small accuracy drop of 2.28%. For a larger dataset like ImageNet it can compress the ResNet-50 architecture by 44.7% and reduces FLOPs by 42.3%, with just a 0.69% drop on Top-5 accuracy of the uncompressed model. We also demonstrate that DECORE can be used to search for compressed network architectures based on various constraints, such as memory and FLOPs.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Alwani_DECORE_Deep_Compression_With_Reinforcement_Learning_CVPR_2022_paper.pdf
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http://arxiv.org/abs/2106.06091
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Alwani_DECORE_Deep_Compression_With_Reinforcement_Learning_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Alwani_DECORE_Deep_Compression_With_Reinforcement_Learning_CVPR_2022_paper.html
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CVPR 2022
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Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving
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Peixuan Li, Jieyu Jin
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While separately leveraging monocular 3D object detection and 2D multi-object tracking can be straightforwardly applied to sequence images in a frame-by-frame fashion, stand-alone tracker cuts off the transmission of the uncertainty from the 3D detector to tracking while cannot pass tracking error differentials back to the 3D detector. In this work, we propose jointly training 3D detection and 3D tracking from only monocular videos in an end-to-end manner. The key component is a novel spatial-temporal information flow module that aggregates geometric and appearance features to predict robust similarity scores across all objects in current and past frames. Specifically, we leverage the attention mechanism of the transformer, in which self-attention aggregates the spatial information in a specific frame, and cross-attention exploits relation and affinities of all objects in the temporal domain of sequence frames. The affinities are then supervised to estimate the trajectory and guide the flow of information between corresponding 3D objects. In addition, we propose a temporal-consistency loss that explicitly involves 3D target motion modeling into the learning, making the 3D trajectory smooth in the world coordinate system. Time3D achieves 21.4% AMOTA, 13.6% AMOTP on the nuScenes 3D tracking benchmark, surpassing all published competitors, and running at 38 FPS, while Time3D achieves 31.2% mAP, 39.4% NDS on the nuScenes 3D detection benchmark.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Time3D_End-to-End_Joint_Monocular_3D_Object_Detection_and_Tracking_for_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Time3D_End-to-End_Joint_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2205.14882
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_Time3D_End-to-End_Joint_Monocular_3D_Object_Detection_and_Tracking_for_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Li_Time3D_End-to-End_Joint_Monocular_3D_Object_Detection_and_Tracking_for_CVPR_2022_paper.html
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CVPR 2022
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MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
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Qing Lian, Peiliang Li, Xiaozhi Chen
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Due to the inherent ill-posed nature of 2D-3D projection, monocular 3D object detection lacks accurate depth recovery ability. Although the deep neural network (DNN) enables monocular depth-sensing from high-level learned features, the pixel-level cues are usually omitted due to the deep convolution mechanism. To benefit from both the powerful feature representation in DNN and pixel-level geometric constraints, we reformulate the monocular object depth estimation as a progressive refinement problem and propose a joint semantic and geometric cost volume to model the depth error. Specifically, we first leverage neural networks to learn the object position, dimension, and dense normalized 3D object coordinates. Based on the object depth, the dense coordinates patch together with the corresponding object features is reprojected to the image space to build a cost volume in a joint semantic and geometric error manner. The final depth is obtained by feeding the cost volume to a refinement network, where the distribution of semantic and geometric error is regularized by direct depth supervision. Through effectively mitigating depth error by the refinement framework, we achieve state-of-the-art results on both the KITTI and Waymo datasets.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Lian_MonoJSG_Joint_Semantic_and_Geometric_Cost_Volume_for_Monocular_3D_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lian_MonoJSG_Joint_Semantic_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.08563
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Lian_MonoJSG_Joint_Semantic_and_Geometric_Cost_Volume_for_Monocular_3D_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Lian_MonoJSG_Joint_Semantic_and_Geometric_Cost_Volume_for_Monocular_3D_CVPR_2022_paper.html
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CVPR 2022
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Task Discrepancy Maximization for Fine-Grained Few-Shot Classification
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SuBeen Lee, WonJun Moon, Jae-Pil Heo
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Recognizing discriminative details such as eyes and beaks is important for distinguishing fine-grained classes since they have similar overall appearances. In this regard, we introduce Task Discrepancy Maximization (TDM), a simple module for fine-grained few-shot classification. Our objective is to localize the class-wise discriminative regions by highlighting channels encoding distinct information of the class. Specifically, TDM learns task-specific channel weights based on two novel components: Support Attention Module (SAM) and Query Attention Module (QAM). SAM produces a support weight to represent channel-wise discriminative power for each class. Still, since the SAM is basically only based on the labeled support sets, it can be vulnerable to bias toward such support set. Therefore, we propose QAM which complements SAM by yielding a query weight that grants more weight to object-relevant channels for a given query image. By combining these two weights, a class-wise task-specific channel weight is defined. The weights are then applied to produce task-adaptive feature maps more focusing on the discriminative details. Our experiments validate the effectiveness of TDM and its complementary benefits with prior methods in fine-grained few-shot classification.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Task_Discrepancy_Maximization_for_Fine-Grained_Few-Shot_Classification_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Task_Discrepancy_Maximization_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Task_Discrepancy_Maximization_for_Fine-Grained_Few-Shot_Classification_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Task_Discrepancy_Maximization_for_Fine-Grained_Few-Shot_Classification_CVPR_2022_paper.html
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CVPR 2022
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FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction
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Liang Gao, Huazhu Fu, Li Li, Yingwen Chen, Ming Xu, Cheng-Zhong Xu
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Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity among their local data distributions, which would cause inconsistent optimized local models on the client-side. To address this fundamental dilemma, we propose a novel federated learning algorithm with local drift decoupling and correction (FedDC). Our FedDC only introduces lightweight modifications in the local training phase, in which each client utilizes an auxiliary local drift variable to track the gap between the local model parameter and the global model parameters. The key idea of FedDC is to utilize this learned local drift variable to bridge the gap, i.e., conducting consistency in parameter-level. The experiment results and analysis demonstrate that FedDC yields expediting convergence and better performance on various image classification tasks, robust in partial participation settings, non-iid data, and heterogeneous clients.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_FedDC_Federated_Learning_With_Non-IID_Data_via_Local_Drift_Decoupling_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gao_FedDC_Federated_Learning_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.11751
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Gao_FedDC_Federated_Learning_With_Non-IID_Data_via_Local_Drift_Decoupling_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Gao_FedDC_Federated_Learning_With_Non-IID_Data_via_Local_Drift_Decoupling_CVPR_2022_paper.html
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CVPR 2022
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Efficient Classification of Very Large Images With Tiny Objects
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Fanjie Kong, Ricardo Henao
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An increasing number of applications in computer vision, specially, in medical imaging and remote sensing, become challenging when the goal is to classify very large images with tiny informative objects. Specifically, these classification tasks face two key challenges: i) the size of the input image is usually in the order of mega- or giga-pixels, however, existing deep architectures do not easily operate on such big images due to memory constraints, consequently, we seek a memory-efficient method to process these images; and ii) only a very small fraction of the input images are informative of the label of interest, resulting in low region of interest (ROI) to image ratio. However, most of the current convolutional neural networks (CNNs) are designed for image classification datasets that have relatively large ROIs and small image sizes (sub-megapixel). Existing approaches have addressed these two challenges in isolation. We present an end-to-end CNN model termed Zoom-In network that leverages hierarchical attention sampling for classification of large images with tiny objects using a single GPU. We evaluate our method on four large-image histopathology, road-scene and satellite imaging datasets, and one gigapixel pathology dataset. Experimental results show that our model achieves higher accuracy than existing methods while requiring less memory resources.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Kong_Efficient_Classification_of_Very_Large_Images_With_Tiny_Objects_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kong_Efficient_Classification_of_CVPR_2022_supplemental.zip
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http://arxiv.org/abs/2106.02694
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Efficient_Classification_of_Very_Large_Images_With_Tiny_Objects_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Efficient_Classification_of_Very_Large_Images_With_Tiny_Objects_CVPR_2022_paper.html
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CVPR 2022
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SWEM: Towards Real-Time Video Object Segmentation With Sequential Weighted Expectation-Maximization
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Zhihui Lin, Tianyu Yang, Maomao Li, Ziyu Wang, Chun Yuan, Wenhao Jiang, Wei Liu
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Matching-based methods, especially those based on space-time memory, are significantly ahead of other solutions in semi-supervised video object segmentation (VOS). However, continuously growing and redundant template features lead to an inefficient inference. To alleviate this, we propose a novel Sequential Weighted Expectation-Maximization (SWEM) network to greatly reduce the redundancy of memory features. Different from the previous methods which only detect feature redundancy between frames, SWEM merges both intra-frame and inter-frame similar features by leveraging the sequential weighted EM algorithm. Further, adaptive weights for frame features endow SWEM with the flexibility to represent hard samples, improving the discrimination of templates. Besides, the proposed method maintains a fixed number of template features in memory, which ensures the stable inference complexity of the VOS system. Extensive experiments on commonly used DAVIS and YouTube-VOS datasets verify the high efficiency (36 FPS) and high performance (84.3% J&F on DAVIS 2017 validation dataset) of SWEM.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_SWEM_Towards_Real-Time_Video_Object_Segmentation_With_Sequential_Weighted_Expectation-Maximization_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_SWEM_Towards_Real-Time_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Lin_SWEM_Towards_Real-Time_Video_Object_Segmentation_With_Sequential_Weighted_Expectation-Maximization_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Lin_SWEM_Towards_Real-Time_Video_Object_Segmentation_With_Sequential_Weighted_Expectation-Maximization_CVPR_2022_paper.html
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CVPR 2022
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Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation
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Yuenan Hou, Xinge Zhu, Yuexin Ma, Chen Change Loy, Yikang Li
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This article addresses the problem of distilling knowledge from a large teacher model to a slim student network for LiDAR semantic segmentation. Directly employing previous distillation approaches yields inferior results due to the intrinsic challenges of point cloud, i.e., sparsity, randomness and varying density. To tackle the aforementioned problems, we propose the Point-to-Voxel Knowledge Distillation (PVD), which transfers the hidden knowledge from both point level and voxel level. Specifically, we first leverage both the pointwise and voxelwise output distillation to complement the sparse supervision signals. Then, to better exploit the structural information, we divide the whole point cloud into several supervoxels and design a difficultyaware sampling strategy to more frequently sample supervoxels containing less frequent classes and faraway objects. On these supervoxels, we propose inter-point and intervoxel affinity distillation, where the similarity information between points and voxels can help the student model better capture the structural information of the surrounding environment. We conduct extensive experiments on two popular LiDAR segmentation benchmarks, i.e., nuScenes [3] and SemanticKITTI [1]. On both benchmarks, our PVD consistently outperforms previous distillation approaches by a large margin on three representative backbones, i.e., Cylinder3D [27, 28], SPVNAS [20] and MinkowskiNet [5]. Notably, on the challenging nuScenes and SemanticKITTI datasets, our method can achieve roughly 75% MACs reduction and 2x speedup on the competitive Cylinder3D model and rank 1st on the SemanticKITTI leaderboard among all published algorithms. Our code is available at https://github.com/cardwing/Codes-for-PVKD.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Hou_Point-to-Voxel_Knowledge_Distillation_for_LiDAR_Semantic_Segmentation_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hou_Point-to-Voxel_Knowledge_Distillation_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Hou_Point-to-Voxel_Knowledge_Distillation_for_LiDAR_Semantic_Segmentation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Hou_Point-to-Voxel_Knowledge_Distillation_for_LiDAR_Semantic_Segmentation_CVPR_2022_paper.html
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CVPR 2022
| null |
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers
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Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell
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Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups). Extending the bias-variance decomposition for classification to fairness, we theoretically explain why the majority of fairness methods designed for low capacity models should not be used in settings involving high-capacity models, a scenario common to computer vision. We corroborate this analysis with extensive experimental support that shows that many of the fairness heuristics used in computer vision also degrade performance on the most disadvantaged groups. Building on these insights, we propose an adaptive augmentation strategy that, uniquely, of all methods tested, improves performance for the disadvantaged groups.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zietlow_Leveling_Down_in_Computer_Vision_Pareto_Inefficiencies_in_Fair_Deep_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zietlow_Leveling_Down_in_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.04913
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zietlow_Leveling_Down_in_Computer_Vision_Pareto_Inefficiencies_in_Fair_Deep_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zietlow_Leveling_Down_in_Computer_Vision_Pareto_Inefficiencies_in_Fair_Deep_CVPR_2022_paper.html
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CVPR 2022
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Generating Diverse 3D Reconstructions From a Single Occluded Face Image
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Rahul Dey, Vishnu Naresh Boddeti
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Occlusions are a common occurrence in unconstrained face images. Single image 3D reconstruction from such face images often suffers from corruption due to the presence of occlusions. Furthermore, while a plurality of 3D reconstructions is plausible in the occluded regions, existing approaches are limited to generating only a single solution. To address both of these challenges, we present Diverse3DFace, which is specifically designed to simultaneously generate a diverse and realistic set of 3D reconstructions from a single occluded face image. It consists of three components: a global+local shape fitting process, a graph neural network-based mesh VAE, and a Determinantal Point Process based diversity promoting iterative optimization procedure. Quantitative and qualitative comparisons of 3D reconstruction on occluded faces show that Diverse3DFace can estimate 3D shapes that are consistent with the visible regions in the target image while exhibiting high, yet realistic, levels of diversity on the occluded regions. On face images occluded by masks, glasses, and other random objects, Diverse3DFace generates a distribution of 3D shapes having 50% higher diversity on the occluded regions compared to the baselines. Moreover, our closest sample to the ground truth has 40% lower MSE than the singular reconstructions by existing approaches. Code and data available at: https://github.com/human-analysis/diverse3dface
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https://openaccess.thecvf.com/content/CVPR2022/papers/Dey_Generating_Diverse_3D_Reconstructions_From_a_Single_Occluded_Face_Image_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dey_Generating_Diverse_3D_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.00879
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Dey_Generating_Diverse_3D_Reconstructions_From_a_Single_Occluded_Face_Image_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Dey_Generating_Diverse_3D_Reconstructions_From_a_Single_Occluded_Face_Image_CVPR_2022_paper.html
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CVPR 2022
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RBGNet: Ray-Based Grouping for 3D Object Detection
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Haiyang Wang, Shaoshuai Shi, Ze Yang, Rongyao Fang, Qi Qian, Hongsheng Li, Bernt Schiele, Liwei Wang
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As a fundamental problem in computer vision, 3D object detection is experiencing rapid growth. To extract the point-wise features from the irregularly and sparsely distributed points, previous methods usually take a feature grouping module to aggregate the point features to an object candidate. However, these methods have not yet leveraged the surface geometry of foreground objects to enhance grouping and 3D box generation. In this paper, we propose the RBGNet framework, a voting-based 3D detector for accurate 3D object detection from point clouds. In order to learn better representations of object shape to enhance cluster features for predicting 3D boxes, we propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays uniformly emitted from cluster centers. Considering the fact that foreground points are more meaningful for box estimation, we design a novel foreground biased sampling strategy in downsample process to sample more points on object surfaces and further boost the detection performance. Our model achieves state-of-the-art 3D detection performance on ScanNet V2 and SUN RGB-D with remarkable performance gains.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RBGNet_Ray-Based_Grouping_for_3D_Object_Detection_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_RBGNet_Ray-Based_Grouping_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2204.02251
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_RBGNet_Ray-Based_Grouping_for_3D_Object_Detection_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_RBGNet_Ray-Based_Grouping_for_3D_Object_Detection_CVPR_2022_paper.html
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CVPR 2022
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Stand-Alone Inter-Frame Attention in Video Models
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Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Jiebo Luo, Tao Mei
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Motion, as the uniqueness of a video, has been critical to the development of video understanding models. Modern deep learning models leverage motion by either executing spatio-temporal 3D convolutions, factorizing 3D convolutions into spatial and temporal convolutions separately, or computing self-attention along temporal dimension. The implicit assumption behind such successes is that the feature maps across consecutive frames can be nicely aggregated. Nevertheless, the assumption may not always hold especially for the regions with large deformation. In this paper, we present a new recipe of inter-frame attention block, namely Stand-alone Inter-Frame Attention (SIFA), that novelly delves into the deformation across frames to estimate local self-attention on each spatial location. Technically, SIFA remoulds the deformable design via re-scaling the offset predictions by the difference between two frames. Taking each spatial location in the current frame as the query, the locally deformable neighbors in the next frame are regarded as the keys/values. Then, SIFA measures the similarity between query and keys as stand-alone attention to weighted average the values for temporal aggregation. We further plug SIFA block into ConvNets and Vision Transformer, respectively, to devise SIFA-Net and SIFA-Transformer. Extensive experiments conducted on four video datasets demonstrate the superiority of SIFA-Net and SIFA-Transformer as stronger backbones. More remarkably, SIFA-Transformer achieves an accuracy of 83.1% on Kinetics-400 dataset. Source code is available at https://github.com/FuchenUSTC/SIFA.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Long_Stand-Alone_Inter-Frame_Attention_in_Video_Models_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Long_Stand-Alone_Inter-Frame_Attention_in_Video_Models_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Long_Stand-Alone_Inter-Frame_Attention_in_Video_Models_CVPR_2022_paper.html
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CVPR 2022
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Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation
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Jogendra Nath Kundu, Siddharth Seth, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu
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The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in the absence of any provision to discard unfamiliar out-of-distribution data. To this end, we cast the 3D human pose learning as an unsupervised domain adaptation problem. We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations; a) model-free joint localization and b) model-based parametric regression. Such a design allows us to derive suitable measures to quantify prediction uncertainty at both pose and joint level granularity. While supervising only on labeled synthetic samples, the adaptation process aims to minimize the uncertainty for the unlabeled target images while maximizing the same for an extreme out-of-distribution dataset (backgrounds). Alongside synthetic-to-real 3D pose adaptation, the joint-uncertainties allow expanding the adaptation to work on in-the-wild images even in the presence of occlusion and truncation scenarios. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Kundu_Uncertainty-Aware_Adaptation_for_Self-Supervised_3D_Human_Pose_Estimation_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kundu_Uncertainty-Aware_Adaptation_for_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.15293
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Kundu_Uncertainty-Aware_Adaptation_for_Self-Supervised_3D_Human_Pose_Estimation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Kundu_Uncertainty-Aware_Adaptation_for_Self-Supervised_3D_Human_Pose_Estimation_CVPR_2022_paper.html
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CVPR 2022
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Open-Domain, Content-Based, Multi-Modal Fact-Checking of Out-of-Context Images via Online Resources
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Sahar Abdelnabi, Rakibul Hasan, Mario Fritz
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Misinformation is now a major problem due to its potential high risks to our core democratic and societal values and orders. Out-of-context misinformation is one of the easiest and effective ways used by adversaries to spread viral false stories. In this threat, a real image is re-purposed to support other narratives by misrepresenting its context and/or elements. The internet is being used as the go-to way to verify information using different sources and modalities. Our goal is an inspectable method that automates this time-consuming and reasoning-intensive process by fact-checking the image-caption pairing using Web evidence. To integrate evidence and cues from both modalities, we introduce the concept of 'multi-modal cycle-consistency check'; starting from the image/caption, we gather textual/visual evidence, which will be compared against the other paired caption/image, respectively. Moreover, we propose a novel architecture, Consistency-Checking Network (CCN), that mimics the layered human reasoning across the same and different modalities: the caption vs. textual evidence, the image vs. visual evidence, and the image vs. caption. Our work offers the first step and benchmark for open-domain, content-based, multi-modal fact-checking, and significantly outperforms previous baselines that did not leverage external evidence.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Abdelnabi_Open-Domain_Content-Based_Multi-Modal_Fact-Checking_of_Out-of-Context_Images_via_Online_Resources_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Abdelnabi_Open-Domain_Content-Based_Multi-Modal_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.00061
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Abdelnabi_Open-Domain_Content-Based_Multi-Modal_Fact-Checking_of_Out-of-Context_Images_via_Online_Resources_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Abdelnabi_Open-Domain_Content-Based_Multi-Modal_Fact-Checking_of_Out-of-Context_Images_via_Online_Resources_CVPR_2022_paper.html
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CVPR 2022
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Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening
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Gang Yang, Man Zhou, Keyu Yan, Aiping Liu, Xueyang Fu, Fan Wang
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Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success. However, most existing methods are designed in a black-box principle, lacking sufficient interpretability. Additionally, they ignore the different characteristics of each band of MS images and directly concatenate them with panchromatic (PAN) images, leading to severe copy artifacts. To address the above issues, we propose an interpretable deep neural network, namely Memory-augmented Deep Conditional Unfolding Network with two specified core designs. Firstly, considering the degradation process, it formulates the Pan-sharpening problem as the minimization of a variational model with denoising-based prior and non-local auto-regression prior which is capable of searching the similarities between long-range patches, benefiting the texture enhancement. A novel iteration algorithm with built-in CNNs is exploited for transparent model design. Secondly, to fully explore the potentials of different bands of MS images, the PAN image is combined with each band of MS images, selectively providing the high-frequency details and alleviating the copy artifacts. Extensive experimental results validate the superiority of the proposed algorithm against other state-of-the-art methods.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Memory-Augmented_Deep_Conditional_Unfolding_Network_for_Pan-Sharpening_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Memory-Augmented_Deep_Conditional_Unfolding_Network_for_Pan-Sharpening_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Memory-Augmented_Deep_Conditional_Unfolding_Network_for_Pan-Sharpening_CVPR_2022_paper.html
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CVPR 2022
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Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer
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Fushun Zhu, Shan Zhao, Peng Wang, Hao Wang, Hua Yan, Shuaicheng Liu
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We propose a semi-supervised network for wide-angle portraits correction. Wide-angle images often suffer from skew and distortion affected by perspective distortion, especially noticeable at the face regions. Previous deep learning based approaches need the ground-truth correction flow maps for training guidance. However, such labels are expensive, which can only be obtained manually. In this work, we design a semi-supervised scheme and build a high-quality unlabeled dataset with rich scenarios, allowing us to simultaneously use labeled and unlabeled data to improve performance. Specifically, our semi-supervised scheme takes advantage of the consistency mechanism, with several novel components such as direction and range consistency (DRC) and regression consistency (RC). Furthermore, different from the existing methods, we propose the Multi-Scale Swin-Unet (MS-Unet) based on the multi-scale swin transformer block (MSTB), which can simultaneously learn short-distance and long-distance information to avoid artifacts. Extensive experiments demonstrate that the proposed method is superior to the state-of-the-art methods and other representative baselines.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Semi-Supervised_Wide-Angle_Portraits_Correction_by_Multi-Scale_Transformer_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Semi-Supervised_Wide-Angle_Portraits_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2109.08024
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Semi-Supervised_Wide-Angle_Portraits_Correction_by_Multi-Scale_Transformer_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Semi-Supervised_Wide-Angle_Portraits_Correction_by_Multi-Scale_Transformer_CVPR_2022_paper.html
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CVPR 2022
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Large-Scale Pre-Training for Person Re-Identification With Noisy Labels
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Dengpan Fu, Dongdong Chen, Hao Yang, Jianmin Bao, Lu Yuan, Lei Zhang, Houqiang Li, Fang Wen, Dong Chen
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This paper aims to address the problem of pre-training for person re-identification (Re-ID) with noisy labels. To setup the pre-training task, we apply a simple online multi-object tracking system on raw videos of an existing unlabeled Re-ID dataset "LUPerson" and build the Noisy Labeled variant called "LUPerson-NL". Since theses ID labels automatically derived from tracklets inevitably contain noises, we develop a large-scale Pre-training framework utilizing Noisy Labels (PNL), which consists of three learning modules: supervised Re-ID learning, prototype-based contrastive learning, and label-guided contrastive learning. In principle, joint learning of these three modules not only clusters similar examples to one prototype, but also rectifies noisy labels based on the prototype assignment. We demonstrate that learning directly from raw videos is a promising alternative for pre-training, which utilizes spatial and temporal correlations as weak supervision. This simple pre-training task provides a scalable way to learn SOTA Re-ID representations from scratch on "LUPerson-NL" without bells and whistles. For example, by applying on the same supervised Re-ID method MGN, our pre-trained model improves the mAP over the unsupervised pre-training counterpart by 5.7%, 2.2%, 2.3% on CUHK03, DukeMTMC, and MSMT17 respectively. Under the small-scale or few-shot setting, the performance gain is even more significant, suggesting a better transferability of the learned representation. Code is available at https://github.com/DengpanFu/LUPerson-NL
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https://openaccess.thecvf.com/content/CVPR2022/papers/Fu_Large-Scale_Pre-Training_for_Person_Re-Identification_With_Noisy_Labels_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fu_Large-Scale_Pre-Training_for_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.16533
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Fu_Large-Scale_Pre-Training_for_Person_Re-Identification_With_Noisy_Labels_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Fu_Large-Scale_Pre-Training_for_Person_Re-Identification_With_Noisy_Labels_CVPR_2022_paper.html
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CVPR 2022
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Adiabatic Quantum Computing for Multi Object Tracking
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Jan-Nico Zaech, Alexander Liniger, Martin Danelljan, Dengxin Dai, Luc Van Gool
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Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. The association step naturally leads to discrete optimization problems. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. Adiabatic quantum computing (AQC) offers a solution for this, as it has the potential to provide a considerable speedup on a range of NP-hard optimization problems in the near future. However, current MOT formulations are unsuitable for quantum computing due to their scaling properties. In this work, we therefore propose the first MOT formulation designed to be solved with AQC. We employ an Ising model that represents the quantum mechanical system implemented on the AQC. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers. Finally, we demonstrate that our MOT problem is already solvable on the current generation of real quantum computers for small examples, and analyze the properties of the measured solutions.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zaech_Adiabatic_Quantum_Computing_for_Multi_Object_Tracking_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zaech_Adiabatic_Quantum_Computing_CVPR_2022_supplemental.zip
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http://arxiv.org/abs/2202.08837
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zaech_Adiabatic_Quantum_Computing_for_Multi_Object_Tracking_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zaech_Adiabatic_Quantum_Computing_for_Multi_Object_Tracking_CVPR_2022_paper.html
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CVPR 2022
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Feature Erasing and Diffusion Network for Occluded Person Re-Identification
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Zhikang Wang, Feng Zhu, Shixiang Tang, Rui Zhao, Lihuo He, Jiangning Song
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Occluded person re-identification (ReID) aims at matching occluded person images to holistic ones across different camera views. Target Pedestrians (TP) are often disturbed by Non-Pedestrian Occlusions (NPO) and Non-Target Pedestrians (NTP). Previous methods mainly focus on increasing the model's robustness against NPO while ignoring feature contamination from NTP. In this paper, we propose a novel Feature Erasing and Diffusion Network (FED) to simultaneously handle challenges from NPO and NTP. Specifically, aided by the NPO augmentation strategy that simulates NPO on holistic pedestrian images and generates precise occlusion masks, NPO features are explicitly eliminated by our proposed Occlusion Erasing Module (OEM). Subsequently, we diffuse the pedestrian representations with other memorized features to synthesize the NTP characteristics in the feature space through the novel Feature Diffusion Module (FDM). With the guidance of the occlusion scores from OEM, the feature diffusion process is conducted on visible body parts, thereby improving the quality of the synthesized NTP characteristics. We can greatly improve the model's perception ability towards TP and alleviate the influence of NPO and NTP by jointly optimizing OEM and FDM. Furthermore, the proposed FDM works as an auxiliary module for training and will not be engaged in the inference phase, thus with high flexibility. Experiments on occluded and holistic person ReID benchmarks demonstrate the superiority of FED over state-of-the-art methods.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Feature_Erasing_and_Diffusion_Network_for_Occluded_Person_Re-Identification_CVPR_2022_paper.pdf
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http://arxiv.org/abs/2112.08740
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Feature_Erasing_and_Diffusion_Network_for_Occluded_Person_Re-Identification_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Feature_Erasing_and_Diffusion_Network_for_Occluded_Person_Re-Identification_CVPR_2022_paper.html
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CVPR 2022
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Is Mapping Necessary for Realistic PointGoal Navigation?
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Ruslan Partsey, Erik Wijmans, Naoki Yokoyama, Oles Dobosevych, Dhruv Batra, Oleksandr Maksymets
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Can an autonomous agent navigate in a new environment without building an explicit map? For the task of PointGoal navigation ('Go to (x, y)') under idealized settings (no RGB-D and actuation noise, perfect GPS+Compass), the answer is a clear 'yes' - map-less neural models composed of task-agnostic components (CNNs and RNNs) trained with large-scale reinforcement learning achieve 100% Success on a standard dataset (Gibson). However, for PointNav in a realistic setting (RGB-D and actuation noise, no GPS+Compass), this is an open question; one we tackle in this paper. The strongest published result for this task is 71.7% Success. First, we identify the main (perhaps, only) cause of the drop in performance: absence of GPS+Compass. An agent with perfect GPS+Compass faced with RGB-D sensing and actuation noise achieves 99.8% Success (Gibson-v2 val). This suggests that (to paraphrase a meme) robust visual odometry is all we need for realistic PointNav; if we can achieve that, we can ignore the sensing and actuation noise. With that as our operating hypothesis, we scale dataset size, model size, and develop human-annotation-free data-augmentation techniques to train neural models for visual odometry. We advance state of the art on the Habitat Realistic PointNav Challenge - SPL by 40% (relative), 53 to 74, and Success by 31% (relative), 71 to 94. While our approach does not saturate or 'solve' this dataset, this strong improvement combined with promising zero-shot sim2real transfer (to a LoCoBot robot) provides evidence consistent with the hypothesis that explicit mapping may not be necessary for navigation, even in realistic setting.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Partsey_Is_Mapping_Necessary_for_Realistic_PointGoal_Navigation_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Partsey_Is_Mapping_Necessary_CVPR_2022_supplemental.zip
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http://arxiv.org/abs/2206.00997
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Partsey_Is_Mapping_Necessary_for_Realistic_PointGoal_Navigation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Partsey_Is_Mapping_Necessary_for_Realistic_PointGoal_Navigation_CVPR_2022_paper.html
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CVPR 2022
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Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification
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Yonghang Guan, Jun Zhang, Kuan Tian, Sen Yang, Pei Dong, Jinxi Xiang, Wei Yang, Junzhou Huang, Yuyao Zhang, Xiao Han
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The large-scale whole-slide images (WSIs) facilitate the learning-based computational pathology methods. However, the gigapixel size of WSIs makes it hard to train a conventional model directly. Current approaches typically adopt multiple-instance learning (MIL) to tackle this problem. Among them, MIL combined with graph convolutional network (GCN) is a significant branch, where the sampled patches are regarded as the graph nodes to further discover their correlations. However, it is difficult to build correspondence across patches from different WSIs. Therefore, most methods have to perform non-ordered node pooling to generate the bag-level representation. Direct non-ordered pooling will lose much structural and contextual information, such as patch distribution and heterogeneous patterns, which is critical for WSI representation. In this paper, we propose a hierarchical global-to-local clustering strategy to build a Node-Aligned GCN (NAGCN) to represent WSI with rich local structural information as well as global distribution. We first deploy a global clustering operation based on the instance features in the dataset to build the correspondence across different WSIs. Then, we perform a local clustering-based sampling strategy to select typical instances belonging to each cluster within the WSI. Finally, we employ the graph convolution to obtain the representation. Since our graph construction strategy ensures the alignment among different WSIs, WSI-level representation can be easily generated and used for the subsequent classification. The experiment results on two cancer subtype classification datasets demonstrate our method achieves better performance compared with the state-of-the-art methods.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Guan_Node-Aligned_Graph_Convolutional_Network_for_Whole-Slide_Image_Representation_and_Classification_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Guan_Node-Aligned_Graph_Convolutional_Network_for_Whole-Slide_Image_Representation_and_Classification_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Guan_Node-Aligned_Graph_Convolutional_Network_for_Whole-Slide_Image_Representation_and_Classification_CVPR_2022_paper.html
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CVPR 2022
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Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting
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Min Shi, Hao Lu, Chen Feng, Chengxin Liu, Zhiguo Cao
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Class-agnostic counting (CAC) aims to count all instances in a query image given few exemplars. A standard pipeline is to extract visual features from exemplars and match them with query images to infer object counts. Two essential components in this pipeline are feature representation and similarity metric. Existing methods either adopt a pretrained network to represent features or learn a new one, while applying a naive similarity metric with fixed inner product. We find this paradigm leads to noisy similarity matching and hence harms counting performance. In this work, we propose a similarity-aware CAC framework that jointly learns representation and similarity metric. We first instantiate our framework with a naive baseline called Bilinear Matching Network (BMNet), whose key component is a learnable bilinear similarity metric. To further embody the core of our framework, we extend BMNet to BMNet+ that models similarity from three aspects: 1) representing the instances via their self-similarity to enhance feature robustness against intra-class variations; 2) comparing the similarity dynamically to focus on the key patterns of each exemplar; 3) learning from a supervision signal to impose explicit constraints on matching results. Extensive experiments on a recent CAC dataset FSC147 show that our models significantly outperform state-of-the-art CAC approaches. In addition, we also validate the cross-dataset generality of BMNet and BMNet+ on a car counting dataset CARPK. Code is at tiny.one/BMNet
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https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_Represent_Compare_and_Learn_A_Similarity-Aware_Framework_for_Class-Agnostic_Counting_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shi_Represent_Compare_and_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.08354
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Represent_Compare_and_Learn_A_Similarity-Aware_Framework_for_Class-Agnostic_Counting_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Represent_Compare_and_Learn_A_Similarity-Aware_Framework_for_Class-Agnostic_Counting_CVPR_2022_paper.html
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CVPR 2022
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Masked Feature Prediction for Self-Supervised Visual Pre-Training
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Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, Christoph Feichtenhofer
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We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models. Our approach first randomly masks out a portion of the input sequence and then predicts the feature of the masked regions. We study five different types of features and find Histograms of Oriented Gradients (HOG), a hand-crafted feature descriptor, works particularly well in terms of both performance and efficiency. We observe that the local contrast normalization in HOG is essential for good results, which is in line with earlier work using HOG for visual recognition. Our approach can learn abundant visual knowledge and drive large-scale Transformer-based models. Without using extra model weights or supervision, MaskFeat pre-trained on unlabeled videos achieves unprecedented results of 86.7% with MViTv2-L on Kinetics-400, 88.3% on Kinetics-600, 80.4% on Kinetics-700, 38.8 mAP on AVA, and 75.0% on SSv2. MaskFeat further generalizes to image input, which can be interpreted as a video with a single frame and obtains competitive results on ImageNet.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wei_Masked_Feature_Prediction_for_Self-Supervised_Visual_Pre-Training_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wei_Masked_Feature_Prediction_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.09133
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Masked_Feature_Prediction_for_Self-Supervised_Visual_Pre-Training_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Masked_Feature_Prediction_for_Self-Supervised_Visual_Pre-Training_CVPR_2022_paper.html
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CVPR 2022
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Critical Regularizations for Neural Surface Reconstruction in the Wild
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Jingyang Zhang, Yao Yao, Shiwei Li, Tian Fang, David McKinnon, Yanghai Tsin, Long Quan
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Neural implicit functions have recently shown promising results on surface reconstructions from multiple views. However, current methods still suffer from excessive time complexity and poor robustness when reconstructing unbounded or complex scenes. In this paper, we present RegSDF, which shows that proper point cloud supervisions and geometry regularizations are sufficient to produce high-quality and robust reconstruction results. Specifically, RegSDF takes an additional oriented point cloud as input, and optimizes a signed distance field and a surface light field within a differentiable rendering framework. We also introduce the two critical regularizations for this optimization. The first one is the Hessian regularization that smoothly diffuses the signed distance values to the entire distance field given noisy and incomplete input. And the second one is the minimal surface regularization that compactly interpolates and extrapolates the missing geometry. Extensive experiments are conducted on DTU, BlendedMVS, and Tanks and Temples datasets. Compared with recent neural surface reconstruction approaches, RegSDF is able to reconstruct surfaces with fine details even for open scenes with complex topologies and unstructured camera trajectories.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Critical_Regularizations_for_Neural_Surface_Reconstruction_in_the_Wild_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Critical_Regularizations_for_CVPR_2022_supplemental.zip
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Critical_Regularizations_for_Neural_Surface_Reconstruction_in_the_Wild_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Critical_Regularizations_for_Neural_Surface_Reconstruction_in_the_Wild_CVPR_2022_paper.html
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CVPR 2022
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EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot Learning
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Hao Zhu, Piotr Koniusz
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Few-shot learning (FSL) has received a lot of attention due to its remarkable ability to adapt to novel classes. Although many techniques have been proposed for FSL, they mostly focus on improving FSL backbones. Some works also focus on learning on top of the features generated by these backbones to adapt them to novel classes. We present an unsupErvised discriminAnt Subspace lEarning (EASE) that improves transductive few-shot learning performance by learning a linear projection onto a subspace built from features of the support set and the unlabeled query set in the test time. Specifically, based on the support set and the unlabeled query set, we generate the similarity matrix and the dissimilarity matrix based on the structure prior for the proposed EASE method, which is efficiently solved with SVD. We also introduce conStraIned wAsserstein MEan Shift clustEring (SIAMESE) which extends Sinkhorn K-means by incorporating labeled support samples. SIAMESE works on the features obtained from EASE to estimate class centers and query predictions. On the mini-ImageNet, tiered-ImageNet, CIFAR-FS, CUB and OpenMIC benchmarks, both steps significantly boost the performance in transductive FSL and semi-supervised FSL.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_EASE_Unsupervised_Discriminant_Subspace_Learning_for_Transductive_Few-Shot_Learning_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_EASE_Unsupervised_Discriminant_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_EASE_Unsupervised_Discriminant_Subspace_Learning_for_Transductive_Few-Shot_Learning_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_EASE_Unsupervised_Discriminant_Subspace_Learning_for_Transductive_Few-Shot_Learning_CVPR_2022_paper.html
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CVPR 2022
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Object-Relation Reasoning Graph for Action Recognition
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Yangjun Ou, Li Mi, Zhenzhong Chen
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Action recognition is a challenging task since the attributes of objects as well as their relationships change constantly in the video. Existing methods mainly use object-level graphs or scene graphs to represent the dynamics of objects and relationships, but ignore modeling the fine-grained relationship transitions directly. In this paper, we propose an Object-Relation Reasoning Graph (OR2G) for reasoning about action in videos. By combining an object-level graph (OG) and a relation-level graph (RG), the proposed OR2G catches the attribute transitions of objects and reasons about the relationship transitions between objects simultaneously. In addition, a graph aggregating module (GAM) is investigated by applying the multi-head edge-to-node message passing operation. GAM feeds back the information from the relation node to the object node and enhances the coupling between the object-level graph and the relation-level graph. Experiments in video action recognition demonstrate the effectiveness of our approach when compared with the state-of-the-art methods.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Ou_Object-Relation_Reasoning_Graph_for_Action_Recognition_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Ou_Object-Relation_Reasoning_Graph_for_Action_Recognition_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Ou_Object-Relation_Reasoning_Graph_for_Action_Recognition_CVPR_2022_paper.html
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CVPR 2022
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Semantic Segmentation by Early Region Proxy
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Yifan Zhang, Bo Pang, Cewu Lu
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Typical vision backbones manipulate structured features. As a compromise, semantic segmentation has long been modeled as per-point prediction on dense regular grids. In this work, we present a novel and efficient modeling that starts from interpreting the image as a tessellation of learnable regions, each of which has flexible geometrics and carries homogeneous semantics. To model region-wise context, we exploit Transformer to encode regions in a sequence-to-sequence manner by applying multi-layer self-attention on the region embeddings, which serve as proxies of specific regions. Semantic segmentation is now carried out as per-region prediction on top of the encoded region embeddings using a single linear classifier, where a decoder is no longer needed. The proposed RegProxy model discards the common Cartesian feature layout and operates purely at region level. Hence, it exhibits the most competitive performance-efficiency trade-off compared with the conventional dense prediction methods. For example, on ADE20K, the small-sized RegProxy-S/16 outperforms the best CNN model using 25% parameters and 4% computation, while the largest RegProxy-L/16 achieves 52.9mIoU which outperforms the state-of-the-art by 2.1% with fewer resources. Codes and models are available at https://github.com/YiF-Zhang/RegionProxy.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Semantic_Segmentation_by_Early_Region_Proxy_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Semantic_Segmentation_by_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.14043
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Semantic_Segmentation_by_Early_Region_Proxy_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Semantic_Segmentation_by_Early_Region_Proxy_CVPR_2022_paper.html
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CVPR 2022
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GIQE: Generic Image Quality Enhancement via Nth Order Iterative Degradation
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Pranjay Shyam, Kyung-Soo Kim, Kuk-Jin Yoon
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Visual degradations caused by motion blur, raindrop, rain, snow, illumination, and fog deteriorate image quality and, subsequently, the performance of perception algorithms deployed in outdoor conditions. While degradation-specific image restoration techniques have been extensively studied, such algorithms are domain sensitive and fail in real scenarios where multiple degradations exist simultaneously. This makes a case for blind image restoration and reconstruction algorithms as practically relevant. However, the absence of a dataset diverse enough to encapsulate all variations hinders development for such an algorithm. In this paper, we utilize a synthetic degradation model that recursively applies sets of random degradations to generate naturalistic degradation images of varying complexity, which are used as input. Furthermore, as the degradation intensity can vary across an image, the spatially invariant convolutional filter cannot be applied for all degradations. Hence to enable spatial variance during image restoration and reconstruction, we design a transformer-based architecture to benefit from the long-range dependencies. In addition, to reduce the computational cost of transformers, we propose a multi-branch structure coupled with modifications such as a complimentary feature selection mechanism and the replacement of a feed-forward network with lightweight multiscale convolutions. Finally, to improve restoration and reconstruction, we integrate an auxiliary decoder branch to predict the degradation mask to ensure the underlying network can localize the degradation information. From empirical analysis on 10 datasets covering rain drop removal, deraining, dehazing, image enhancement, and deblurring, we demonstrate the efficacy of the proposed approach while obtaining SoTA performance.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Shyam_GIQE_Generic_Image_Quality_Enhancement_via_Nth_Order_Iterative_Degradation_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shyam_GIQE_Generic_Image_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Shyam_GIQE_Generic_Image_Quality_Enhancement_via_Nth_Order_Iterative_Degradation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Shyam_GIQE_Generic_Image_Quality_Enhancement_via_Nth_Order_Iterative_Degradation_CVPR_2022_paper.html
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CVPR 2022
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Instance Segmentation With Mask-Supervised Polygonal Boundary Transformers
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Justin Lazarow, Weijian Xu, Zhuowen Tu
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In this paper, we present an end-to-end instance segmentation method that regresses a polygonal boundary for each object instance. This sparse, vectorized boundary representation for objects, while attractive in many downstream computer vision tasks, quickly runs into issues of parity that need to be addressed: parity in supervision and parity in performance when compared to existing pixel-based methods. This is due in part to object instances being annotated with ground-truth in the form of polygonal boundaries or segmentation masks, yet being evaluated in a convenient manner using only segmentation masks. Our method, named BoundaryFormer, is a Transformer based architecture that directly predicts polygons yet uses instance mask segmentations as the ground-truth supervision for computing the loss. We achieve this by developing an end-to-end differentiable model that solely relies on supervision within the mask space through differentiable rasterization. BoundaryFormer matches or surpasses the Mask R-CNN method in terms of instance segmentation quality on both COCO and Cityscapes while exhibiting significantly better transferability across datasets.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Lazarow_Instance_Segmentation_With_Mask-Supervised_Polygonal_Boundary_Transformers_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Lazarow_Instance_Segmentation_With_Mask-Supervised_Polygonal_Boundary_Transformers_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Lazarow_Instance_Segmentation_With_Mask-Supervised_Polygonal_Boundary_Transformers_CVPR_2022_paper.html
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CVPR 2022
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FaceVerse: A Fine-Grained and Detail-Controllable 3D Face Morphable Model From a Hybrid Dataset
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Lizhen Wang, Zhiyuan Chen, Tao Yu, Chenguang Ma, Liang Li, Yebin Liu
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We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed to take better advantage of our hybrid dataset. In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc. Then in the fine module, a conditional StyleGAN architecture trained with high-fidelity scan models is introduced to enrich elaborate facial geometric and texture details. Note that different from previous methods, our base and detailed modules are both changeable, which enables an innovative application of adjusting both the basic attributes and the facial details of 3D face models. Furthermore, we propose a single-image fitting framework based on differentiable rendering. Rich experiments show that our method outperforms the state-of-the-art methods.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_FaceVerse_A_Fine-Grained_and_Detail-Controllable_3D_Face_Morphable_Model_From_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_FaceVerse_A_Fine-Grained_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.14057
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_FaceVerse_A_Fine-Grained_and_Detail-Controllable_3D_Face_Morphable_Model_From_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_FaceVerse_A_Fine-Grained_and_Detail-Controllable_3D_Face_Morphable_Model_From_CVPR_2022_paper.html
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CVPR 2022
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Bring Evanescent Representations to Life in Lifelong Class Incremental Learning
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Marco Toldo, Mete Ozay
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In Class Incremental Learning (CIL), a classification model is progressively trained at each incremental step on an evolving dataset of new classes, while at the same time, it is required to preserve knowledge of all the classes observed so far. Prototypical representations can be leveraged to model feature distribution for the past data and inject information of former classes in later incremental steps without resorting to stored exemplars. However, if not updated, those representations become increasingly outdated as the incremental learning progresses with new classes. To address the aforementioned problems, we propose a framework which aims to (i) model the semantic drift by learning the relationship between representations of past and novel classes among incremental steps, and (ii) estimate the feature drift, defined as the evolution of the representations learned by models at each incremental step. Semantic and feature drifts are then jointly exploited to infer up-to-date representations of past classes (evanescent representations), and thereby infuse past knowledge into incremental training. We experimentally evaluate our framework achieving exemplar-free SotA results on multiple benchmarks. In the ablation study, we investigate nontrivial relationships between evanescent representations and models.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Toldo_Bring_Evanescent_Representations_to_Life_in_Lifelong_Class_Incremental_Learning_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Toldo_Bring_Evanescent_Representations_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Toldo_Bring_Evanescent_Representations_to_Life_in_Lifelong_Class_Incremental_Learning_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Toldo_Bring_Evanescent_Representations_to_Life_in_Lifelong_Class_Incremental_Learning_CVPR_2022_paper.html
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CVPR 2022
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Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures With Uncalibrated Stereo Data
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Nikolay Patakin, Anna Vorontsova, Mikhail Artemyev, Anton Konushin
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Nowadays, robotics, AR, and 3D modeling applications attract considerable attention to single-view depth estimation (SVDE) as it allows estimating scene geometry from a single RGB image. Recent works have demonstrated that the accuracy of an SVDE method hugely depends on the diversity and volume of the training data. However, RGB-D datasets obtained via depth capturing or 3D reconstruction are typically small, synthetic datasets are not photorealistic enough, and all these datasets lack diversity. The large-scale and diverse data can be sourced from stereo images or stereo videos from the web. Typically being uncalibrated, stereo data provides disparities up to unknown shift (geometrically incomplete data), so stereo-trained SVDE methods cannot recover 3D geometry. It was recently shown that the distorted point clouds obtained with a stereo-trained SVDE method can be corrected with additional point cloud modules (PCM) separately trained on the geometrically complete data. On the contrary, we propose GP2, General-Purpose and Geometry-Preserving training scheme, and show that conventional SVDE models can learn correct shifts themselves without any post-processing, benefiting from using stereo data even in the geometry-preserving setting. Through experiments on different dataset mixtures, we prove that GP2-trained models outperform methods relying on PCM in both accuracy and speed, and report the state-of-the-art results in the general-purpose geometry-preserving SVDE. Moreover, we show that SVDE models can learn to predict geometrically correct depth even when geometrically complete data comprises the minor part of the training set.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Patakin_Single-Stage_3D_Geometry-Preserving_Depth_Estimation_Model_Training_on_Dataset_Mixtures_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Patakin_Single-Stage_3D_Geometry-Preserving_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Patakin_Single-Stage_3D_Geometry-Preserving_Depth_Estimation_Model_Training_on_Dataset_Mixtures_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Patakin_Single-Stage_3D_Geometry-Preserving_Depth_Estimation_Model_Training_on_Dataset_Mixtures_CVPR_2022_paper.html
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CVPR 2022
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LD-ConGR: A Large RGB-D Video Dataset for Long-Distance Continuous Gesture Recognition
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Dan Liu, Libo Zhang, Yanjun Wu
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Gesture recognition plays an important role in natural human-computer interaction and sign language recognition. Existing research on gesture recognition is limited to close-range interaction such as vehicle gesture control and face-to-face communication. To apply gesture recognition to long-distance interactive scenes such as meetings and smart homes, a large RGB-D video dataset LD-ConGR is established in this paper. LD-ConGR is distinguished from existing gesture datasets by its long-distance gesture collection, fine-grained annotations, and high video quality. Specifically, 1) the farthest gesture provided by the LD-ConGR is captured 4m away from the camera while existing gesture datasets collect gestures within 1m from the camera; 2) besides the gesture category, the temporal segmentation of gestures and hand location are also annotated in LD-ConGR; 3) videos are captured at high resolution (1280x720 for color streams and 640x576 for depth streams) and high frame rate (30 fps). On top of the LD-ConGR, a series of experimental and studies are conducted, and the proposed gesture region estimation and key frame sampling strategies are demonstrated to be effective in dealing with long-distance gesture recognition and the uncertainty of gesture duration. The dataset and experimental results presented in this paper are expected to boost the research of long-distance gesture recognition. The dataset is available at https://github.com/Diananini/LD-ConGR-CVPR2022.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_LD-ConGR_A_Large_RGB-D_Video_Dataset_for_Long-Distance_Continuous_Gesture_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_LD-ConGR_A_Large_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Liu_LD-ConGR_A_Large_RGB-D_Video_Dataset_for_Long-Distance_Continuous_Gesture_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Liu_LD-ConGR_A_Large_RGB-D_Video_Dataset_for_Long-Distance_Continuous_Gesture_CVPR_2022_paper.html
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CVPR 2022
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SimVQA: Exploring Simulated Environments for Visual Question Answering
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Paola Cascante-Bonilla, Hui Wu, Letao Wang, Rogerio S. Feris, Vicente Ordonez
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Existing work on VQA explores data augmentation to achieve better generalization by perturbing the images in the dataset or modifying the existing questions and answers. While these methods exhibit good performance, the diversity of the questions and answers are constrained by the available image set. In this work we explore using synthetic computer-generated data to fully control the visual and language space, allowing us to provide more diverse scenarios. We quantify the effect of synthetic data in real-world VQA benchmarks and to which extent it produces results that generalize to real data. By exploiting 3D and physics simulation platforms, we provide a pipeline to generate synthetic data to expand and replace type-specific questions and answers without risking the exposure of sensitive or personal data that might be present in real images. We offer a comprehensive analysis while expanding existing hyper-realistic datasets to be used for VQA. We also propose Feature Swapping (F-SWAP) -- where we randomly switch object-level features during training to make a VQA model more domain invariant. We show that F-SWAP is effective for enhancing a currently existing VQA dataset of real images without compromising on the accuracy to answer existing questions in the dataset.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Cascante-Bonilla_SimVQA_Exploring_Simulated_Environments_for_Visual_Question_Answering_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cascante-Bonilla_SimVQA_Exploring_Simulated_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Cascante-Bonilla_SimVQA_Exploring_Simulated_Environments_for_Visual_Question_Answering_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Cascante-Bonilla_SimVQA_Exploring_Simulated_Environments_for_Visual_Question_Answering_CVPR_2022_paper.html
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CVPR 2022
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Thin-Plate Spline Motion Model for Image Animation
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Jian Zhao, Hui Zhang
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Image animation brings life to the static object in the source image according to the driving video. Recent works attempt to perform motion transfer on arbitrary objects through unsupervised methods without using a priori knowledge. However, it remains a significant challenge for current unsupervised methods when there is a large pose gap between the objects in the source and driving images. In this paper, a new end-to-end unsupervised motion transfer framework is proposed to overcome such issue. Firstly, we propose thin-plate spline motion estimation to produce a more flexible optical flow, which warps the feature maps of the source image to the feature domain of the driving image. Secondly, in order to restore the missing regions more realistically, we leverage multi-resolution occlusion masks to achieve more effective feature fusion. Finally, additional auxiliary loss functions are designed to ensure that there is a clear division of labor in the network modules, encouraging the network to generate high-quality images. Our method can animate a variety of objects, including talking faces, human bodies, and pixel animations. Experiments demonstrate that our method performs better on most benchmarks than the state of the art with visible improvements in pose-related metrics.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Thin-Plate_Spline_Motion_Model_for_Image_Animation_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Thin-Plate_Spline_Motion_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.14367
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Thin-Plate_Spline_Motion_Model_for_Image_Animation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Thin-Plate_Spline_Motion_Model_for_Image_Animation_CVPR_2022_paper.html
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CVPR 2022
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Learning Local Displacements for Point Cloud Completion
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Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari
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We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. Our architecture relies on three novel layers that are used successively within an encoder-decoder structure and specifically developed for the task at hand. The first one carries out feature extraction by matching the point features to a set of pre-trained local descriptors. Then, to avoid losing individual descriptors as part of standard operations such as max-pooling, we propose an alternative neighbor-pooling operation that relies on adopting the feature vectors with the highest activations. Finally, up-sampling in the decoder modifies our feature extraction in order to increase the output dimension. While this model is already able to achieve competitive results with the state of the art, we further propose a way to increase the versatility of our approach to process point clouds. To this aim, we introduce a second model that assembles our layers within a transformer architecture. We evaluate both architectures on object and indoor scene completion tasks, achieving state-of-the-art performance.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Learning_Local_Displacements_for_Point_Cloud_Completion_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Learning_Local_Displacements_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.16600
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_Local_Displacements_for_Point_Cloud_Completion_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_Local_Displacements_for_Point_Cloud_Completion_CVPR_2022_paper.html
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CVPR 2022
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Human Hands As Probes for Interactive Object Understanding
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Mohit Goyal, Sahil Modi, Rishabh Goyal, Saurabh Gupta
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Interactive object understanding, or what we can do to objects and how is a long-standing goal of computer vision. In this paper, we tackle this problem through observation of human hands in in-the-wild egocentric videos. We demonstrate that observation of what human hands interact with and how can provide both the relevant data and the necessary supervision. Attending to hands, readily localizes and stabilizes active objects for learning and reveals places where interactions with objects occur. Analyzing the hands shows what we can do to objects and how. We apply these basic principles on the EPIC-KITCHENS dataset, and successfully learn state-sensitive features, and object affordances (regions of interaction and afforded grasps), purely by observing hands in egocentric videos.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Goyal_Human_Hands_As_Probes_for_Interactive_Object_Understanding_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Goyal_Human_Hands_As_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.09120
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Goyal_Human_Hands_As_Probes_for_Interactive_Object_Understanding_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Goyal_Human_Hands_As_Probes_for_Interactive_Object_Understanding_CVPR_2022_paper.html
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CVPR 2022
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Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training
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Theodoros Tsiligkaridis, Jay Roberts
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Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique that approximately solves a robust optimization problem to minimize the worst-case loss and is widely regarded as the most effective defense against such attacks. Due to the high computation time for generating strong adversarial examples in the AT process, single-step approaches have been proposed to reduce training time. However, these methods suffer from catastrophic overfitting where adversarial accuracy drops during training, and although improvements have been proposed, they increase training time and robustness is far from that of multi-step AT. We develop a theoretical framework for adversarial training with FW optimization (FW-AT) that reveals a geometric connection between the loss landscape and the distortion of l-inf FW attacks (the attack's l-2 norm). Specifically, we analytically show that high distortion of FW attacks is equivalent to small gradient variation along the attack path. It is then experimentally demonstrated on various deep neural network architectures that l-inf attacks against robust models achieve near maximal l-2 distortion, while standard networks have lower distortion. Furthermore, it is experimentally shown that catastrophic overfitting is strongly correlated with low distortion of FW attacks. This mathematical transparency differentiates FW from the more popular Projected Gradient Descent (PGD) optimization. To demonstrate the utility of our theoretical framework we develop FW-AT-Adapt, a novel adversarial training algorithm which uses a simple distortion measure to adapt the number of attack steps during training to increase efficiency without compromising robustness. FW-AT-Adapt provides training time on par with single-step fast AT methods and improves closing the gap between fast AT methods and multi-step PGD-AT with minimal loss in adversarial accuracy in white-box and black-box settings.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Tsiligkaridis_Understanding_and_Increasing_Efficiency_of_Frank-Wolfe_Adversarial_Training_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tsiligkaridis_Understanding_and_Increasing_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2012.12368
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Tsiligkaridis_Understanding_and_Increasing_Efficiency_of_Frank-Wolfe_Adversarial_Training_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Tsiligkaridis_Understanding_and_Increasing_Efficiency_of_Frank-Wolfe_Adversarial_Training_CVPR_2022_paper.html
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CVPR 2022
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Certified Patch Robustness via Smoothed Vision Transformers
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Hadi Salman, Saachi Jain, Eric Wong, Aleksander Madry
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Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region. But, currently, this robustness comes at a cost of degraded standard accuracies and slower inference times. We demonstrate how using vision transformers enables significantly better certified patch robustness that is also more computationally efficient and does not incur a substantial drop in standard accuracy. These improvements stem from the inherent ability of the vision transformer to gracefully handle largely masked images.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Salman_Certified_Patch_Robustness_via_Smoothed_Vision_Transformers_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Salman_Certified_Patch_Robustness_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2110.07719
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Salman_Certified_Patch_Robustness_via_Smoothed_Vision_Transformers_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Salman_Certified_Patch_Robustness_via_Smoothed_Vision_Transformers_CVPR_2022_paper.html
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CVPR 2022
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Look Back and Forth: Video Super-Resolution With Explicit Temporal Difference Modeling
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Takashi Isobe, Xu Jia, Xin Tao, Changlin Li, Ruihuang Li, Yongjie Shi, Jing Mu, Huchuan Lu, Yu-Wing Tai
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Temporal modeling is crucial for video super-resolution. Most of the video super-resolution methods adopt the optical flow or deformable convolution for explicitly motion compensation. However, such temporal modeling techniques increase the model complexity and might fail in case of occlusion or complex motion, resulting in serious distortion and artifacts. In this paper, we propose to explore the role of explicit temporal difference modeling in both LR and HR space. Instead of directly feeding consecutive frames into a VSR model, we propose to compute the temporal difference between frames and divide those pixels into two subsets according to the level of difference. They are separately processed with two branches of different receptive fields in order to better extract complementary information. To further enhance the super-resolution result, not only spatial residual features are extracted, but the difference between consecutive frames in high-frequency domain is also computed. It allows the model to exploit intermediate SR results in both future and past to refine the current SR output. The difference at different time steps could be cached such that information from further distance in time could be propagated to the current frame for refinement. Experiments on several video super-resolution benchmark datasets demonstrate the effectiveness of the proposed method and its favorable performance against state-of-the-art methods.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Isobe_Look_Back_and_Forth_Video_Super-Resolution_With_Explicit_Temporal_Difference_CVPR_2022_paper.pdf
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http://arxiv.org/abs/2204.07114
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Isobe_Look_Back_and_Forth_Video_Super-Resolution_With_Explicit_Temporal_Difference_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Isobe_Look_Back_and_Forth_Video_Super-Resolution_With_Explicit_Temporal_Difference_CVPR_2022_paper.html
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CVPR 2022
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UCC: Uncertainty Guided Cross-Head Co-Training for Semi-Supervised Semantic Segmentation
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Jiashuo Fan, Bin Gao, Huan Jin, Lihui Jiang
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Deep neural networks (DNNs) have witnessed great successes in semantic segmentation, which requires a large number of labeled data for training. We present a novel learning framework called Uncertainty guided Cross-head Co-training (UCC) for semi-supervised semantic segmentation. Our framework introduces weak and strong augmentations within a shared encoder to achieve co-training, which naturally combines the benefits of consistency and self-training. Every segmentation head interacts with its peers and, the weak augmentation result is used for supervising the strong. The consistency training samples' diversity can be boosted by Dynamic Cross-Set Copy-Paste (DCSCP), which also alleviates the distribution mismatch and class imbalance problems. Moreover, our proposed Uncertainty Guided Re-weight Module (UGRM) enhances the self-training pseudo labels by suppressing the effect of the low-quality pseudo labels from its peer via modeling uncertainty. Extensive experiments on Cityscapes and PASCAL VOC 2012 demonstrate the effectiveness of our UCC, our approach significantly outperforms other state-of-the-art semi-supervised semantic segmentation methods. It achieves 77.17%, 76.49% mIoU on Cityscapes and PASCAL VOC 2012 datasets respectively under 1/16 protocols, which are +10.1%, +7.91% better than the supervised baseline.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_UCC_Uncertainty_Guided_Cross-Head_Co-Training_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf
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http://arxiv.org/abs/2205.10334
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Fan_UCC_Uncertainty_Guided_Cross-Head_Co-Training_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Fan_UCC_Uncertainty_Guided_Cross-Head_Co-Training_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.html
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CVPR 2022
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HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture
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Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Michael Zollhöfer, Jessica Hodgins, Christoph Lassner
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Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, complex physical interaction and the non-trivial visual appearance that must be captured. Yet, it is a critical component to create believable avatars. In this paper, we address the aforementioned problems: 1) we use a novel, volumetric hair representation that is composed of thousands of primitives. Each primitive can be rendered efficiently, yet realistically, by building on the latest advances in neural rendering. 2) To have a reliable control signal, we present a novel way of tracking hair on strand level. To keep the computational effort manageable, we use guide hairs and classic techniques to expand those into a dense head of hair. 3) To better enforce temporal consistency and generalization ability of our model, we further optimize the 3D scene flow of our representation with multiview optical flow, using volumetric raymarching. Our method can not only create realistic renders of recorded multi-view sequences, but also create renderings for new hair configurations by providing new control signals. We compare our method with existing work on viewpoint synthesis and drivable animation and achieve state-of-the-art results. https://ziyanw1.github.io/hvh/
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_HVH_Learning_a_Hybrid_Neural_Volumetric_Representation_for_Dynamic_Hair_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_HVH_Learning_a_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2112.06904
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_HVH_Learning_a_Hybrid_Neural_Volumetric_Representation_for_Dynamic_Hair_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_HVH_Learning_a_Hybrid_Neural_Volumetric_Representation_for_Dynamic_Hair_CVPR_2022_paper.html
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CVPR 2022
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RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising
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Michael Schelling, Pedro Hermosilla, Timo Ropinski
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Time-of-Flight (ToF) cameras are subject to high levels of noise and distortions due to Multi-Path-Interference (MPI). While recent research showed that 2D neural networks are able to outperform previous traditional State-of-the-Art (SOTA) methods on correcting ToF-Data, little research on learning-based approaches has been done to make direct use of the 3D information present in depth images. In this paper, we propose an iterative correcting approach operating in 3D space, that is designed to learn on 2.5D data by enabling 3D point convolutions to correct the points' positions along the view direction. As labeled real world data is scarce for this task, we further train our network with a self-training approach on unlabeled real world data to account for real world statistics. We demonstrate that our method is able to outperform SOTA methods on several datasets, including two real world datasets and a new large-scale synthetic data set introduced in this paper.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Schelling_RADU_Ray-Aligned_Depth_Update_Convolutions_for_ToF_Data_Denoising_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Schelling_RADU_Ray-Aligned_Depth_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2111.15513
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Schelling_RADU_Ray-Aligned_Depth_Update_Convolutions_for_ToF_Data_Denoising_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Schelling_RADU_Ray-Aligned_Depth_Update_Convolutions_for_ToF_Data_Denoising_CVPR_2022_paper.html
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CVPR 2022
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Rethinking Visual Geo-Localization for Large-Scale Applications
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Gabriele Berton, Carlo Masone, Barbara Caputo
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Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations. To investigate how existing techniques would perform on a real-world city-wide VG application, we build San Francisco eXtra Large, a new dataset covering a whole city and providing a wide range of challenging cases, with a size 30x bigger than the previous largest dataset for visual geo-localization. We find that current methods fail to scale to such large datasets, therefore we design a new highly scalable training technique, called CosPlace, which casts the training as a classification problem avoiding the expensive mining needed by the commonly used contrastive learning. We achieve state-of-the-art performance on a wide range of datasets, and find that CosPlace is robust to heavy domain changes. Moreover, we show that, compared to previous state of the art, CosPlace requires roughly 80% less GPU memory at train time and achieves better results with 8x smaller descriptors, paving the way for city-wide real-world visual geo-localization. Dataset, code and trained models are available for research purposes at https://github.com/gmberton/CosPlace.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Berton_Rethinking_Visual_Geo-Localization_for_Large-Scale_Applications_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Berton_Rethinking_Visual_Geo-Localization_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2204.02287
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Berton_Rethinking_Visual_Geo-Localization_for_Large-Scale_Applications_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Berton_Rethinking_Visual_Geo-Localization_for_Large-Scale_Applications_CVPR_2022_paper.html
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CVPR 2022
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Learning Based Multi-Modality Image and Video Compression
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Guo Lu, Tianxiong Zhong, Jing Geng, Qiang Hu, Dong Xu
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Multi-modality (i.e., multi-sensor) data is widely used in various vision tasks for more accurate or robust perception. However, the increased data modalities bring new challenges for data storage and transmission. The existing data compression approaches usually adopt individual codecs for each modality without considering the correlation between different modalities. This work proposes a multi-modality compression framework for infrared and visible image pairs by exploiting the cross-modality redundancy. Specifically, given the image in the reference modality (e.g., the infrared image), we use the channel-wise alignment module to produce the aligned features based on the affine transform. Then the aligned feature is used as the context information for compressing the image in the current modality (e.g., the visible image), and the corresponding affine coefficients are losslessly compressed at negligible cost. Furthermore, we introduce the Transformer-based spatial alignment module to exploit the correlation between the intermediate features in the decoding procedures for different modalities. Our framework is very flexible and easily extended for multi-modality video compression. Experimental results show our proposed framework outperforms the traditional and learning-based single modality compression methods on the FLIR and KAIST datasets.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_Learning_Based_Multi-Modality_Image_and_Video_Compression_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_Learning_Based_Multi-Modality_CVPR_2022_supplemental.pdf
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Learning_Based_Multi-Modality_Image_and_Video_Compression_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Learning_Based_Multi-Modality_Image_and_Video_Compression_CVPR_2022_paper.html
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CVPR 2022
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A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
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Ramya Hebbalaguppe, Jatin Prakash, Neelabh Madan, Chetan Arora
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Deep Neural Networks (DNNs) are known to make overconfident mistakes, which makes their use problematic in safety-critical applications. State-of-the-art (SOTA) calibration techniques improve on the confidence of predicted labels alone, and leave the confidence of non-max classes (e.g. top-2, top-5) uncalibrated. Such calibration is not suitable for label refinement using post-processing. Further, most SOTA techniques learn a few hyper-parameters post-hoc, leaving out the scope for image, or pixel specific calibration. This makes them unsuitable for calibration under domain shift, or for dense prediction tasks like semantic segmentation. In this paper, we argue for intervening at the train time itself, so as to directly produce calibrated DNN models. We propose a novel auxiliary loss function: Multi-class Difference in Confidence and Accuracy (MDCA), to achieve the same. MDCA can be used in conjunction with other application/task specific loss functions. We show that training with MDCA leads to better calibrated models in terms of Expected Calibration Error (ECE), and Static Calibration Error (SCE) on image classification, and segmentation tasks. We report ECE(SCE) score of 0.72 (1.60) on the CIFAR100 dataset, in comparison to 1.90 (1.71) by the SOTA. Under domain shift, a ResNet-18 model trained on PACS dataset using MDCA gives a average ECE(SCE) score of 19.7 (9.7) across all domains, compared to 24.2 (11.8) by the SOTA. For segmentation task, we report a 2x reduction in calibration error on PASCAL-VOC dataset in comparison to Focal Loss. Finally, MDCA training improves calibration even on imbalanced data, and for natural language classification tasks.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Hebbalaguppe_A_Stitch_in_Time_Saves_Nine_A_Train-Time_Regularizing_Loss_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hebbalaguppe_A_Stitch_in_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.13834
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Hebbalaguppe_A_Stitch_in_Time_Saves_Nine_A_Train-Time_Regularizing_Loss_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Hebbalaguppe_A_Stitch_in_Time_Saves_Nine_A_Train-Time_Regularizing_Loss_CVPR_2022_paper.html
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CVPR 2022
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The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy
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Tianlong Chen, Zhenyu Zhang, Yu Cheng, Ahmed Awadallah, Zhangyang Wang
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Vision transformers (ViTs) have gained increasing popularity as they are commonly believed to own higher modeling capacity and representation flexibility, than traditional convolutional networks. However, it is questionable whether such potential has been fully unleashed in practice, as the learned ViTs often suffer from over-smoothening, yielding likely redundant models. Recent works made preliminary attempts to identify and alleviate such redundancy, e.g., via regularizing embedding similarity or re-injecting convolution-like structures. However, a "head-to-toe assessment" regarding the extent of redundancy in ViTs, and how much we could gain by thoroughly mitigating such, has been absent for this field. This paper, for the first time, systematically studies the ubiquitous existence of redundancy at all three levels: patch embedding, attention map, and weight space. In view of them, we advocate a principle of diversity for training ViTs, by presenting corresponding regularizers that encourage the representation diversity and coverage at each of those levels, that enabling capturing more discriminative information. Extensive experiments on ImageNet with a number of ViT backbones validate the effectiveness of our proposals, largely eliminating the observed ViT redundancy and significantly boosting the model generalization. For example, our diversified DeiT obtains 0.70% 1.76% accuracy boosts on ImageNet with highly reduced similarity. Our codes are fully available in https://github.com/VITA-Group/Diverse-ViT.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_The_Principle_of_Diversity_Training_Stronger_Vision_Transformers_Calls_for_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_The_Principle_of_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.06345
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Chen_The_Principle_of_Diversity_Training_Stronger_Vision_Transformers_Calls_for_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Chen_The_Principle_of_Diversity_Training_Stronger_Vision_Transformers_Calls_for_CVPR_2022_paper.html
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CVPR 2022
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Deep Image-Based Illumination Harmonization
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Zhongyun Bao, Chengjiang Long, Gang Fu, Daquan Liu, Yuanzhen Li, Jiaming Wu, Chunxia Xiao
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Integrating a foreground object into a background scenewith illumination harmonization is an important but chal-lenging task in computer vision and augmented reality community. Existing methods mainly focus on foreground andbackground appearance consistency or the foreground object shadow generation, which rarely consider global appearance and illumination harmonization. In this paper,we formulate seamless illumination harmonization as anillumination exchange and aggregation problem. Specifi-cally, we firstly apply a physically-based rendering methodto construct a large-scale, high-quality dataset (named IH)for our task, which contains various types of foreground ob-jects and background scenes with different lighting conditions. Then, we propose a deep image-based illuminationharmonization GAN framework named DIH-GAN, whichmakes full use of a multi-scale attention mechanism and illumination exchange strategy to directly infer mapping rela-tionship between the inserted foreground object and the corresponding background scene. Meanwhile, we also use adversarial learning strategy to further refine the illuminationharmonization result. Our method can not only achieve har-monious appearance and illumination for the foregroundobject but also can generate compelling shadow cast bythe foreground object. Comprehensive experiments on bothour IH dataset and real-world images show that our pro-posed DIH-GAN provides a practical and effective solutionfor image-based object illumination harmonization editing,and validate the superiority of our method against state-of-the-art methods.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Bao_Deep_Image-Based_Illumination_Harmonization_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bao_Deep_Image-Based_Illumination_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2108.00150
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Bao_Deep_Image-Based_Illumination_Harmonization_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Bao_Deep_Image-Based_Illumination_Harmonization_CVPR_2022_paper.html
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CVPR 2022
| null |
ViM: Out-of-Distribution With Virtual-Logit Matching
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Haoqi Wang, Zhizhong Li, Litong Feng, Wayne Zhang
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Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversity of the OOD examples makes such methods fragile. There are OOD samples that are easy to identify in the feature space while hard to distinguish in the logit space and vice versa. Motivated by this observation, we propose a novel OOD scoring method named Virtual-logit Matching (ViM), which combines the class-agnostic score from feature space and the In-Distribution (ID) class-dependent logits. Specifically, an additional logit representing the virtual OOD class is generated from the residual of the feature against the principal space, and then matched with the original logits by a constant scaling. The probability of this virtual logit after softmax is the indicator of OOD-ness. To facilitate the evaluation of large-scale OOD detection in academia, we create a new OOD dataset for ImageNet-1K, which is human-annotated and is 8.8x the size of existing datasets. We conducted extensive experiments, including CNNs and vision transformers, to demonstrate the effectiveness of the proposed ViM score. In particular, using the BiT-S model, our method gets an average AUROC 90.91% on four difficult OOD benchmarks, which is 4% ahead of the best baseline. Code and dataset are available at https://github.com/haoqiwang/vim.
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https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_ViM_Out-of-Distribution_With_Virtual-Logit_Matching_CVPR_2022_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_ViM_Out-of-Distribution_With_CVPR_2022_supplemental.pdf
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http://arxiv.org/abs/2203.10807
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https://openaccess.thecvf.com
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ViM_Out-of-Distribution_With_Virtual-Logit_Matching_CVPR_2022_paper.html
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https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ViM_Out-of-Distribution_With_Virtual-Logit_Matching_CVPR_2022_paper.html
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CVPR 2022
| null |
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