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Invertible Denoising Network: A Light Solution for Real Noise Removal | Yang Liu, Zhenyue Qin, Saeed Anwar, Pan Ji, Dongwoo Kim, Sabrina Caldwell, Tom Gedeon | Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying invertible models to remove noise is challenging because the input is noisy, and the reversed output is clean, following two different distributions. We propose an invertible denoising network, InvDN, to address this challenge. InvDN transforms the noisy input into a low-resolution clean image and a latent representation containing noise. To discard noise and restore the clean image, InvDN replaces the noisy latent representation with another one sampled from a prior distribution during reversion. The denoising performance of InvDN is better than all the existing competitive models, achieving a new state-of-the-art result for the SIDD dataset while enjoying less run time. Moreover, the size of InvDN is far smaller, only having 4.2% of the number of parameters compared to the most recently proposed DANet. Further, via manipulating the noisy latent representation, InvDN is also able to generate noise more similar to the original one. Our code is available at: https://github.com/Yang-Liu1082/InvDN.git. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Invertible_Denoising_Network_A_Light_Solution_for_Real_Noise_Removal_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10546 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Invertible_Denoising_Network_A_Light_Solution_for_Real_Noise_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Invertible_Denoising_Network_A_Light_Solution_for_Real_Noise_Removal_CVPR_2021_paper.html | CVPR 2021 | null | null |
Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction | Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, Chelsea Finn | A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. However, while existing video prediction models have produced promising results on small datasets, they suffer from severe underfitting when trained on large and diverse datasets. To address this underfitting challenge, we first observe that the ability to train larger video prediction models is often bottlenecked by the memory constraints of GPUs or TPUs. In parallel, deep hierarchical latent variable models can produce higher quality predictions by capturing the multi-level stochasticity of future observations, but end-to-end optimization of such models is notably difficult. Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction. We introduce Greedy Hierarchical Variational Autoencoders (GHVAEs), a method that learns high-fidelity video predictions by greedily training each level of a hierarchical autoencoder. In comparison to state-of-the-art models, GHVAEs provide 17-55% gains in prediction performance on four video datasets, a 35-40% higher success rate on real robot tasks, and can improve performance monotonically by simply adding more modules. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Greedy_Hierarchical_Variational_Autoencoders_for_Large-Scale_Video_Prediction_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Greedy_Hierarchical_Variational_Autoencoders_for_Large-Scale_Video_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Greedy_Hierarchical_Variational_Autoencoders_for_Large-Scale_Video_Prediction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Greedy_Hierarchical_Variational_CVPR_2021_supplemental.pdf | null |
Over-the-Air Adversarial Flickering Attacks Against Video Recognition Networks | Roi Pony, Itay Naeh, Shie Mannor | Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter usually use temporal information contained within the video. In this work we present a manipulation scheme for fooling video classifiers by introducing a flickering temporal perturbation that in some cases may be unnoticeable by human observers and is implementable in the real world. After demonstrating the manipulation of action classification of single videos, we generalize the procedure to make universal adversarial perturbation, achieving high fooling ratio. In addition, we generalize the universal perturbation and produce a temporal-invariant perturbation, which can be applied to the video without synchronizing the perturbation to the input. The attack was implemented on several target models and the transferability of the attack was demonstrated. These properties allow us to bridge the gap between simulated environment and real-world application, as will be demonstrated in this paper for the first time for an over-the-air flickering attack. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pony_Over-the-Air_Adversarial_Flickering_Attacks_Against_Video_Recognition_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2002.05123 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pony_Over-the-Air_Adversarial_Flickering_Attacks_Against_Video_Recognition_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pony_Over-the-Air_Adversarial_Flickering_Attacks_Against_Video_Recognition_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pony_Over-the-Air_Adversarial_Flickering_CVPR_2021_supplemental.pdf | null |
Encoder Fusion Network With Co-Attention Embedding for Referring Image Segmentation | Guang Feng, Zhiwei Hu, Lihe Zhang, Huchuan Lu | Recently, referring image segmentation has aroused widespread interest. Previous methods perform the multi-modal fusion between language and vision at the decoding side of the network. And, linguistic feature interacts with visual feature of each scale separately, which ignores the continuous guidance of language to multi-scale visual features. In this work, we propose an encoder fusion network (EFN), which transforms the visual encoder into a multi-modal feature learning network, and uses language to refine the multi-modal features progressively. Moreover, a co-attention mechanism is embedded in the EFN to realize the parallel update of multi-modal features, which can promote the consistent of the cross-modal information representation in the semantic space. Finally, we propose a boundary enhancement module (BEM) to make the network pay more attention to the fine structure. The experiment results on four benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance under different evaluation metrics without any post-processing. | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Encoder_Fusion_Network_With_Co-Attention_Embedding_for_Referring_Image_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.01839 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Encoder_Fusion_Network_With_Co-Attention_Embedding_for_Referring_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Encoder_Fusion_Network_With_Co-Attention_Embedding_for_Referring_Image_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo | Seung-Hwan Baek, Felix Heide | Active stereo cameras that recover depth from structured light captures have become a cornerstone sensor modality for 3D scene reconstruction and understanding tasks across application domains. Active stereo cameras project a pseudo-random dot pattern on object surfaces to extract disparity independently of object texture. Such hand-crafted patterns are designed in isolation from the scene statistics, ambient illumination conditions, and the reconstruction method. In this work, we propose a method to jointly learn structured illumination and reconstruction, parameterized by a diffractive optical element and a neural network, in an end-to-end fashion. To this end, we introduce a differentiable image formation model for active stereo, relying on both wave and geometric optics, and a trinocular reconstruction network. The jointly optimized pattern, which we dub "Polka Lines," together with the reconstruction network, makes accurate active-stereo depth estimates across imaging conditions. We validate the proposed method in simulation and using with an experimental prototype, and we demonstrate several variants of the Polka Lines patterns specialized to the illumination conditions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13117 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Baek_Polka_Lines_Learning_CVPR_2021_supplemental.zip | null |
Image Inpainting With External-Internal Learning and Monochromic Bottleneck | Tengfei Wang, Hao Ouyang, Qifeng Chen | Although recent inpainting approaches have demonstrated significant improvement with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt colors when filling in the missing regions. To address these issues, we propose an external-internal inpainting scheme with a monochromic bottleneck that helps image inpainting models remove these artifacts. In the external learning stage, we reconstruct missing structures and details in the monochromic space to reduce the learning dimension. In the internal learning stage, we propose a novel internal color propagation method with progressive learning strategies for consistent color restoration. Extensive experiments demonstrate that our proposed scheme helps image inpainting models produce more structure-preserved and visually compelling results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Image_Inpainting_With_External-Internal_Learning_and_Monochromic_Bottleneck_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09068 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Image_Inpainting_With_External-Internal_Learning_and_Monochromic_Bottleneck_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Image_Inpainting_With_External-Internal_Learning_and_Monochromic_Bottleneck_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Image_Inpainting_With_CVPR_2021_supplemental.zip | null |
Patch2Pix: Epipolar-Guided Pixel-Level Correspondences | Qunjie Zhou, Torsten Sattler, Laura Leal-Taixe | The classical matching pipeline used for visual localization typically involves three steps: (i) local feature detection and description, (ii) feature matching, and (iii) outlier rejection. Recently emerged correspondence networks propose to perform those steps inside a single network but suffer from low matching resolution due to the memory bottleneck. In this work, we propose a new perspective to estimate correspondences in a detect-to-refine manner, where we first predict patch-level match proposals and then refine them. We present Patch2Pix, a novel refinement network that refines match proposals by regressing pixel-level matches from the local regions defined by those proposals and jointly rejecting outlier matches with confidence scores. Patch2Pix is weakly supervised to learn correspondences that are consistent with the epipolar geometry of an input image pair. We show that our refinement network significantly improves the performance of correspondence networks on image matching, homography estimation, and localization tasks. In addition, we show that our learned refinement generalizes to fully-supervised methods without re-training, which leads us to state-of-the-art localization performance. The code is available at https://github.com/GrumpyZhou/patch2pix. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_CVPR_2021_supplemental.pdf | null |
Diverse Part Discovery: Occluded Person Re-Identification With Part-Aware Transformer | Yulin Li, Jianfeng He, Tianzhu Zhang, Xiang Liu, Yongdong Zhang, Feng Wu | Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoder-decoder architecture, including a pixel context based transformer encoder and a part prototype based transformer decoder. The proposed PAT model enjoys several merits. First, to the best of our knowledge, this is the first work to exploit the transformer encoder-decoder architecture for occluded person Re-ID in a unified deep model. Second, to learn part prototypes well with only identity labels, we design two effective mechanisms including part diversity and part discriminability. Consequently, we can achieve diverse part discovery for occluded person Re-ID in a weakly supervised manner. Extensive experimental results on six challenging benchmarks for three tasks (occluded, partial and holistic Re-ID) demonstrate that our proposed PAT performs favorably against stat-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Diverse_Part_Discovery_Occluded_Person_Re-Identification_With_Part-Aware_Transformer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.04095 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Diverse_Part_Discovery_Occluded_Person_Re-Identification_With_Part-Aware_Transformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Diverse_Part_Discovery_Occluded_Person_Re-Identification_With_Part-Aware_Transformer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Diverse_Part_Discovery_CVPR_2021_supplemental.pdf | null |
Counterfactual Zero-Shot and Open-Set Visual Recognition | Zhongqi Yue, Tan Wang, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang | We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by only training on the seen-classes. Our idea stems from the observation that the generated samples for unseen-classes are often out of the true distribution, which causes severe recognition rate imbalance between the seen-class (high) and unseen-class (low). We show that the key reason is that the generation is not Counterfactual Faithful, and thus we propose a faithful one, whose generation is from the sample-specific counterfactual question: What would the sample look like, if we set its class attribute to a certain class, while keeping its sample attribute unchanged? Thanks to the faithfulness, we can apply the Consistency Rule to perform unseen/seen binary classification, by asking: Would its counterfactual still look like itself? If "yes", the sample is from a certain class, and "no" otherwise. Through extensive experiments on ZSL and OSR, we demonstrate that our framework effectively mitigates the seen/unseen imbalance and hence significantly improves the overall performance. Note that this framework is orthogonal to existing methods, thus, it can serve as a new baseline to evaluate how ZSL/OSR models generalize. Codes are available at https://github.com/yue-zhongqi/gcm-cf. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yue_Counterfactual_Zero-Shot_and_Open-Set_Visual_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.00887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Counterfactual_Zero-Shot_and_Open-Set_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Counterfactual_Zero-Shot_and_Open-Set_Visual_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yue_Counterfactual_Zero-Shot_and_CVPR_2021_supplemental.pdf | null |
Person30K: A Dual-Meta Generalization Network for Person Re-Identification | Yan Bai, Jile Jiao, Wang Ce, Jun Liu, Yihang Lou, Xuetao Feng, Ling-Yu Duan | Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-driven methods. However, these methods are still not reliable enough for real-world deployments, due to the insufficient generalization capability of the models learned on existing benchmarks that have limitations in multiple aspects, including limited data scale, capture condition variations, and appearance diversities. To this end, we collect a new dataset named Person30K with the following distinct features: 1) a very large scale containing 1.38 million images of 30K identities, 2) a large capture system containing 6,497 cameras deployed at 89 different sites, 3) abundant sample diversities including varied backgrounds and diverse person poses. Furthermore, we propose a domain generalization ReID method, dual-meta generalization network (DMG-Net), to exploit the merits of meta-learning in both the training procedure and the metric space learning. Concretely, we design a "learning then generalization evaluation" meta-training procedure and a meta-discrimination loss to enhance model generalization and discrimination capabilities. Comprehensive experiments validate the effectiveness of our DMG-Net. (Dataset and code will be released.) | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_Person30K_A_Dual-Meta_Generalization_Network_for_Person_Re-Identification_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Person30K_A_Dual-Meta_Generalization_Network_for_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Person30K_A_Dual-Meta_Generalization_Network_for_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | null | null |
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition | Stephen Hausler, Sourav Garg, Ming Xu, Michael Milford, Tobias Fischer | Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world. This paper introduces Patch-NetVLAD, which provides a novel formulation for combining the advantages of both local and global descriptor methods by deriving patch-level features from NetVLAD residuals. Unlike the fixed spatial neighborhood regime of existing local keypoint features, our method enables aggregation and matching of deep-learned local features defined over the feature-space grid. We further introduce a multi-scale fusion of patch features that have complementary scales (i.e. patch sizes) via an integral feature space and show that the fused features are highly invariant to both condition (season, structure, and illumination) and viewpoint (translation and rotation) changes. Patch-NetVLAD achieves state-of-the-art visual place recognition results in computationally limited scenarios, validated on a range of challenging real-world datasets, including winning the Facebook Mapillary Visual Place Recognition Challenge at ECCV2020. It is also adaptable to user requirements, with a speed-optimised version operating over an order of magnitude faster than the state-of-the-art. By combining superior performance with improved computational efficiency in a configurable framework, Patch-NetVLAD is well suited to enhance both stand-alone place recognition capabilities and the overall performance of SLAM systems. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hausler_Patch-NetVLAD_Multi-Scale_Fusion_of_Locally-Global_Descriptors_for_Place_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hausler_Patch-NetVLAD_Multi-Scale_Fusion_of_Locally-Global_Descriptors_for_Place_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hausler_Patch-NetVLAD_Multi-Scale_Fusion_of_Locally-Global_Descriptors_for_Place_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hausler_Patch-NetVLAD_Multi-Scale_Fusion_CVPR_2021_supplemental.pdf | null |
Visually Informed Binaural Audio Generation without Binaural Audios | Xudong Xu, Hang Zhou, Ziwei Liu, Bo Dai, Xiaogang Wang, Dahua Lin | Stereophonic audio, especially binaural audio, plays an essential role in immersive viewing environments. Recent research has explored generating stereophonic audios guided by visual cues and multi-channel audio collections in a fully-supervised manner. However, due to the requirement of professional recording devices, existing datasets are limited in scale and variety, which impedes the generalization of supervised methods to real-world scenarios. In this work, we propose PseudoBinaural, an effective pipeline that is free of binaural recordings. The key insight is to carefully build pseudo visual-stereo pairs with mono data for training. Specifically, we leverage spherical harmonic decomposition and head-related impulse response (HRIR) to identify the relationship between the location of a sound source and the received binaural audio. Then in the visual modality, corresponding visual cues of the mono data are manually placed at sound source positions to form the pairs. Compared to fully-supervised paradigms, our binaural-recording-free pipeline shows great stability in the cross-dataset evaluation and comparable performance under subjective preference. Moreover, combined with binaural recorded data, our method is able to further boost the performance of binaural audio generation under supervised settings. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06162 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Visually_Informed_Binaural_CVPR_2021_supplemental.pdf | null |
Dual Attention Guided Gaze Target Detection in the Wild | Yi Fang, Jiapeng Tang, Wang Shen, Wei Shen, Xiao Gu, Li Song, Guangtao Zhai | Gaze target detection aims to infer where each person in a scene is looking. Existing works focus on 2D gaze and 2D saliency, but fail to exploit 3D contexts. In this work, we propose a three-stage method to simulate the human gaze inference behavior in 3D space. In the first stage, we introduce a coarse-to-fine strategy to robustly estimate a 3D gaze orientation from the head. The predicted gaze is decomposed into a planar gaze on the image plane and a depth-channel gaze. In the second stage, we develop a Dual Attention Module (DAM), which takes the planar gaze to produce the filed of view and masks interfering objects regulated by depth information according to the depth-channel gaze. In the third stage, we use the generated dual attention as guidance to perform two sub-tasks: (1) identifying whether the gaze target is inside or out of the image; (2) locating the target if inside. Extensive experiments demonstrate that our approach performs favorably against state-of-the-art methods on GazeFollow and VideoAttentionTarget datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_Dual_Attention_Guided_Gaze_Target_Detection_in_the_Wild_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_Dual_Attention_Guided_Gaze_Target_Detection_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_Dual_Attention_Guided_Gaze_Target_Detection_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | null | null |
Privacy Preserving Localization and Mapping From Uncalibrated Cameras | Marcel Geppert, Viktor Larsson, Pablo Speciale, Johannes L. Schonberger, Marc Pollefeys | Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem. Our approach simultaneously recovers the intrinsic and extrinsic calibration of a camera from line-features only. This enables uncalibrated devices to both localize themselves within an existing map as well as contribute to the map, while preserving the privacy of the image contents. Furthermore, we also derive a solution to bootstrapping maps from scratch using only uncalibrated devices. Our approach provides comparable performance to the calibrated scenario and the privacy compromising alternatives based on traditional point features. | https://openaccess.thecvf.com/content/CVPR2021/papers/Geppert_Privacy_Preserving_Localization_and_Mapping_From_Uncalibrated_Cameras_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Geppert_Privacy_Preserving_Localization_and_Mapping_From_Uncalibrated_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Geppert_Privacy_Preserving_Localization_and_Mapping_From_Uncalibrated_Cameras_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Geppert_Privacy_Preserving_Localization_CVPR_2021_supplemental.pdf | null |
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling | Wei Ji, Shuang Yu, Junde Wu, Kai Ma, Cheng Bian, Qi Bi, Jingjing Li, Hanruo Liu, Li Cheng, Yefeng Zheng | In medical image analysis, it is typical to collect multiple annotations, each from a different clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated. Meanwhile, from the computer vision practitioner viewpoint, it has been a common practice to adopt the ground-truth obtained via either the majority-vote or simply one annotation from a preferred rater. This process, however, tends to overlook the rich information of agreement or disagreement ingrained in the raw multi-rater annotations. To address this issue, we propose to explicitly model the multi-rater (dis-)agreement, dubbed MRNet, which has two main contributions. First, an expertise-aware inferring module or EIM is devised to embed the expertise level of individual raters as prior knowledge, to form high-level semantic features. Second, our approach is capable of reconstructing multi-rater gradings from coarse predictions, with the multi-rater (dis-)agreement cues being further exploited to improve the segmentation performance. To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation. Extensive empirical experiments are conducted across five medical segmentation tasks of diverse imaging modalities. In these experiments, superior performance of our MRNet is observed comparing to the state-of-the-arts, indicating the effectiveness and applicability of our MRNet toward a wide range of medical segmentation tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ji_Learning_Calibrated_Medical_Image_Segmentation_via_Multi-Rater_Agreement_Modeling_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Learning_Calibrated_Medical_Image_Segmentation_via_Multi-Rater_Agreement_Modeling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Learning_Calibrated_Medical_Image_Segmentation_via_Multi-Rater_Agreement_Modeling_CVPR_2021_paper.html | CVPR 2021 | null | null |
Points As Queries: Weakly Semi-Supervised Object Detection by Points | Liangyu Chen, Tong Yang, Xiangyu Zhang, Wei Zhang, Jian Sun | We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points. It achieves a balance between tremendous annotation burden and detection performance. Based on this setting, we analyze existing detectors and find that these detectors have difficulty in fully exploiting the power of the annotated points. To solve this, we introduce a new detector, Point DETR, which extends DETR by adding a point encoder. Extensive experiments conducted on MS-COCO dataset in various data settings show the effectiveness of our method. In particular, when using 20% fully labeled data from COCO, our detector achieves a promising performance, 33.3 AP, which outperforms a strong baseline (FCOS) by 2.0 AP, and we demonstrate the point annotations bring over 10 points in various AR metrics. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Points_As_Queries_Weakly_Semi-Supervised_Object_Detection_by_Points_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07434 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Points_As_Queries_Weakly_Semi-Supervised_Object_Detection_by_Points_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Points_As_Queries_Weakly_Semi-Supervised_Object_Detection_by_Points_CVPR_2021_paper.html | CVPR 2021 | null | null |
Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network | Ruicheng Feng, Chongyi Li, Huaijin Chen, Shuai Li, Chen Change Loy, Jinwei Gu | Recent development of Under-Display Camera (UDC) systems provides a true bezel-less and notch-free viewing experience on smartphones (and TV, laptops, tablets), while allowing images to be captured from the selfie camera embedded underneath. In a typical UDC system, the microstructure of the semi-transparent organic light-emitting diode (OLED) pixel array attenuates and diffracts the incident light on the camera, resulting in significant image quality degradation. Oftentimes, noise, flare, haze, and blur can be observed in UDC images. In this work, we aim to analyze and tackle the aforementioned degradation problems. We define a physics-based image formation model to better understand the degradation. In addition, we utilize one of the world's first commodity UDC smartphone prototypes to measure the real-world Point Spread Function (PSF) of the UDC system, and provide a model-based data synthesis pipeline to generate realistically degraded images. We specially design a new domain knowledge-enabled Dynamic Skip Connection Network (DISCNet) to restore the UDC images. We demonstrate the effectiveness of our method through extensive experiments on both synthetic and real UDC data. Our physics-based image formation model and proposed DISCNet can provide foundations for further exploration in UDC image restoration, and even for general diffraction artifact removal in a broader sense. | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Removing_Diffraction_Image_Artifacts_in_Under-Display_Camera_via_Dynamic_Skip_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09556 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Removing_Diffraction_Image_Artifacts_in_Under-Display_Camera_via_Dynamic_Skip_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Removing_Diffraction_Image_Artifacts_in_Under-Display_Camera_via_Dynamic_Skip_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Feng_Removing_Diffraction_Image_CVPR_2021_supplemental.pdf | null |
iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression | Shifeng Zhang, Chen Zhang, Ning Kang, Zhenguo Li | It is nontrivial to store rapidly growing big data nowadays, which demands high-performance lossless compression techniques. Likelihood-based generative models have witnessed their success on lossless compression, where flow based models are desirable in allowing exact data likelihood optimisation with bijective mappings. However, common continuous flows are in contradiction with the discreteness of coding schemes, which requires either 1) imposing strict constraints on flow models that degrades the performance or 2) coding numerous bijective mapping errors which reduces the efficiency. In this paper, we investigate volume preserving flows for lossless compression and show that a bijective mapping without error is possible. We propose Numerical Invertible Volume Preserving Flow (iVPF) which is derived from the general volume preserving flows. By introducing novel computation algorithms on flow models, an exact bijective mapping is achieved without any numerical error. We also propose a lossless compression algorithm based on iVPF. Experiments on various datasets show that the algorithm based on iVPF achieves state-of-the-art compression ratio over lightweight compression algorithms. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_iVPF_Numerical_Invertible_Volume_Preserving_Flow_for_Efficient_Lossless_Compression_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_iVPF_Numerical_Invertible_Volume_Preserving_Flow_for_Efficient_Lossless_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_iVPF_Numerical_Invertible_Volume_Preserving_Flow_for_Efficient_Lossless_Compression_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_iVPF_Numerical_Invertible_CVPR_2021_supplemental.pdf | null |
Pose Recognition With Cascade Transformers | Ke Li, Shijie Wang, Xiang Zhang, Yifan Xu, Weijian Xu, Zhuowen Tu | In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Pose_Recognition_With_Cascade_Transformers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06976 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Pose_Recognition_With_Cascade_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Pose_Recognition_With_Cascade_Transformers_CVPR_2021_paper.html | CVPR 2021 | null | null |
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection | Zhenyu Wang, Yali Li, Ye Guo, Lu Fang, Shengjin Wang | In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models. Previous semi-supervised methods based on pseudo labels are severely degenerated by noise and prone to overfit to noisy labels, thus are deficient in learning different unlabeled knowledge well. To address this issue, we propose a data-uncertainty guided multi-phase learning method for semi-supervised object detection. We comprehensively consider divergent types of unlabeled images according to their difficulty levels, utilize them in different phases and ensemble models from different phases together to generate ultimate results. Image uncertainty guided easy data selection and region uncertainty guided RoI Re-weighting are involved in multi-phase learning and enable the detector to concentrate on more certain knowledge. Through extensive experiments on PASCAL VOC and MS COCO, we demonstrate that our method behaves extraordinarily compared to baseline approaches and outperforms them by a large margin, more than 3% on VOC and 2% on COCO. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16368 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Data-Uncertainty_Guided_Multi-Phase_CVPR_2021_supplemental.pdf | null |
Prototype-Guided Saliency Feature Learning for Person Search | Hanjae Kim, Sunghun Joung, Ig-Jae Kim, Kwanghoon Sohn | Existing person search methods integrate person detection and re-identification (re-ID) module into a unified system. Though promising results have been achieved, the misalignment problem, which commonly occurs in person search, limits the discriminative feature representation for re-ID. To overcome this limitation, we introduce a novel framework to learn the discriminative representation by utilizing prototype in OIM loss. Unlike conventional methods using prototype as a representation of person identity, we utilize it as guidance to allow the attention network to consistently highlight multiple instances across different poses. Moreover, we propose a new prototype update scheme with adaptive momentum to increase the discriminative ability across different instances. Extensive ablation experiments demonstrate that our method can significantly enhance the feature discriminative power, outperforming the state-of-the-art results on two person search benchmarks including CUHK-SYSU and PRW. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Prototype-Guided_Saliency_Feature_Learning_for_Person_Search_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Prototype-Guided_Saliency_Feature_Learning_for_Person_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Prototype-Guided_Saliency_Feature_Learning_for_Person_Search_CVPR_2021_paper.html | CVPR 2021 | null | null |
Contrastive Learning for Compact Single Image Dehazing | Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, Lizhuang Ma | Single image dehazing is a challenging ill-posed problem due to the severe information degeneration. However, existing deep learning based dehazing methods only adopt clear images as positive samples to guide the training of dehazing network while negative information is unexploited. Moreover, most of them focus on strengthening the dehazing network with an increase of depth and width, leading to a significant requirement of computation and memory. In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively. CR ensures that the restored image is pulled to closer to the clear image and pushed to far away from the hazy image in the representation space. Furthermore, considering trade-off between performance and memory storage, we develop a compact dehazing network based on autoencoder-like (AE) framework. It involves an adaptive mixup operation and a dynamic feature enhancement module, which can benefit from preserving information flow adaptively and expanding the receptive field to improve the network's transformation capability, respectively. We term our dehazing network with autoencoder and contrastive regularization as AECR-Net. The extensive experiments on synthetic and real-world datasets demonstrate that our AECR-Net surpass the state-of-the-art approaches. The code is released in https://github.com/GlassyWu/AECR-Net. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Contrastive_Learning_for_Compact_Single_Image_Dehazing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09367 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Contrastive_Learning_for_Compact_Single_Image_Dehazing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Contrastive_Learning_for_Compact_Single_Image_Dehazing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Contrastive_Learning_for_CVPR_2021_supplemental.pdf | null |
I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors | Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, Yizhou Yu | Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the region proposal mechanisms and ROI-based instance-level features to design fine-grained feature alignment modules with respect to the foreground objects. However, for one-stage detectors, it is hard or even impossible to obtain explicit instance-level features in the detection pipelines. Motivated by this, we propose an Implicit Instance-Invariant Network (I3Net), which is tailored for adapting one-stage detectors and implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers. Specifically, we facilitate the adaptation from three aspects: (1) Dynamic and Class-Balanced Reweighting (DCBR) strategy, which considers the coexistence of intra-domain and intra-class variations to assign larger weights to those sample-scarce categories and easy-to-adapt samples; (2) Category-aware Object Pattern Matching (COPM) module, which boosts the cross-domain foreground objects matching guided by the categorical information and suppresses the uninformative background features; (3) Regularized Joint Category Alignment (RJCA) module, which jointly enforces the category alignment at different domain-specific layers with a consistency regularization. Experiments reveal that I3Net exceeds the state-of-the-art performance on benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_I3Net_Implicit_Instance-Invariant_Network_for_Adapting_One-Stage_Object_Detectors_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13757 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_I3Net_Implicit_Instance-Invariant_Network_for_Adapting_One-Stage_Object_Detectors_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_I3Net_Implicit_Instance-Invariant_Network_for_Adapting_One-Stage_Object_Detectors_CVPR_2021_paper.html | CVPR 2021 | null | null |
Body Meshes as Points | Jianfeng Zhang, Dongdong Yu, Jun Hao Liew, Xuecheng Nie, Jiashi Feng | We consider the challenging multi-person 3D body mesh estimation task in this work. Existing methods are mostly two-stage based--one stage for person localization and the other stage for individual body mesh estimation, leading to redundant pipelines with high computation cost and degraded performance for complex scenes (e.g., occluded person instances). In this work, we present a single stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance. In particular, BMP adopts a new method that represents multiple person instances as points in the spatial-depth space where each point is associated with one body mesh. Hinging on such representations, BMP can directly predict body meshes for multiple persons in a single stage by concurrently localizing person instance points and estimating the corresponding body meshes. To better reason about depth ordering of all the persons within the same scene, BMP designs a simple yet effective inter-instance ordinal depth loss to obtain depth-coherent body mesh estimation. BMP also introduces a novel keypoint-aware augmentation to enhance model robustness to occluded person instances. Comprehensive experiments on benchmarks Panoptic, MuPoTS-3D and 3DPW clearly demonstrate the state-of-the-art efficiency of BMP for multi-person body mesh estimation, together with outstanding accuracy. Code can be found at: https://github.com/jfzhang95/BMP. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02467 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Body_Meshes_as_CVPR_2021_supplemental.pdf | null |
Pixel-Aligned Volumetric Avatars | Amit Raj, Michael Zollhofer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, Stephen Lombardi | Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. Currently, the highest quality is achieved by volumetric approaches trained in a person-specific manner on multi-view data. These models better represent fine structure, such as hair, compared to simpler mesh-based models. Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters. While such architectures achieve impressive rendering quality, they can not easily be extended to the multi-identity setting. In this paper, we devise a novel approach for predicting volumetric avatars of the human head given just a small number of inputs. We enable generalization across identities by a novel parameterization that combines neural radiance fields with local, pixel-aligned features extracted directly from the inputs, thus side-stepping the need for very deep or complex networks. Our approach is trained in an end-to-end manner solely based on a photometric re-rendering loss without requiring explicit 3D supervision. We demonstrate that our approach outperforms the existing state of the art in terms of quality and is able to generate faithful facial expressions in a multi-identity setting. | https://openaccess.thecvf.com/content/CVPR2021/papers/Raj_Pixel-Aligned_Volumetric_Avatars_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Raj_Pixel-Aligned_Volumetric_Avatars_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Raj_Pixel-Aligned_Volumetric_Avatars_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Raj_Pixel-Aligned_Volumetric_Avatars_CVPR_2021_supplemental.zip | null |
UC2: Universal Cross-Lingual Cross-Modal Vision-and-Language Pre-Training | Mingyang Zhou, Luowei Zhou, Shuohang Wang, Yu Cheng, Linjie Li, Zhou Yu, Jingjing Liu | Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English languages, we introduce UC^2, the first machine translation-augmented framework for cross-lingual cross-modal representation learning. To tackle the scarcity problem of multilingual captions for image datasets, we first augment existing English-only datasets with other languages via machine translation (MT). Then we extend the standard Masked Language Modeling and Image-Text Matching training objectives to multilingual setting, where alignment between different languages is captured through shared visual context (eg. using image as pivot). To facilitate the learning of a joint embedding space of images and all languages of interest, we further propose two novel pre-training tasks, namely Maksed Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM), leveraging MT-enhanced translated data. Evaluation on multilingual image-text retrieval and multilingual visual question answering benchmarks demonstrates that our proposed framework achieves new state of the art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_UC2_Universal_Cross-Lingual_Cross-Modal_Vision-and-Language_Pre-Training_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00332 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_UC2_Universal_Cross-Lingual_Cross-Modal_Vision-and-Language_Pre-Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_UC2_Universal_Cross-Lingual_Cross-Modal_Vision-and-Language_Pre-Training_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_UC2_Universal_Cross-Lingual_CVPR_2021_supplemental.pdf | null |
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification | Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu | We propose a generative model of unordered point sets, such as point clouds, in the forms of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network. The energy function learns a coordinate encoding of each point and then aggregates all individual point features into an energy for the whole point cloud. We show that our model can be derived from the discriminative PointNet. The model can be trained by MCMC-based maximum likelihood learning (as well as its variants), without the help of any assisting networks like those in GANs and VAEs. Unlike most point cloud generator that relies on hand-crafting distance metrics, our model does not rely on hand-crafting distance metric for the point cloud generation, because it synthesizes point clouds by matching observed examples in terms of statistical properties defined by the energy function. Furthermore, we can learn a short-run MCMC toward the energy-based model as a flow-like generator for point cloud reconstruction and interpolation. The learned point cloud representation can be useful for point cloud classification. Experiments demonstrate the advantages of the proposed generative model of point clouds. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.01301 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.html | CVPR 2021 | null | null |
Blur, Noise, and Compression Robust Generative Adversarial Networks | Takuhiro Kaneko, Tatsuya Harada | Generative adversarial networks (GANs) have gained considerable attention owing to their ability to reproduce images. However, they can recreate training images faithfully despite image degradation in the form of blur, noise, and compression, generating similarly degraded images. To solve this problem, the recently proposed noise robust GAN (NR-GAN) provides a partial solution by demonstrating the ability to learn a clean image generator directly from noisy images using a two-generator model comprising image and noise generators. However, its application is limited to noise, which is relatively easy to decompose owing to its additive and reversible characteristics, and its application to irreversible image degradation, in the form of blur, compression, and combination of all, remains a challenge. To address these problems, we propose blur, noise, and compression robust GAN (BNCR-GAN) that can learn a clean image generator directly from degraded images without knowledge of degradation parameters (e.g., blur kernel types, noise amounts, or quality factor values). Inspired by NR-GAN, BNCR-GAN uses a multiple-generator model composed of image, blur-kernel, noise, and quality-factor generators. However, in contrast to NR-GAN, to address irreversible characteristics, we introduce masking architectures adjusting degradation strength values in a data-driven manner using bypasses before and after degradation. Furthermore, to suppress uncertainty caused by the combination of blur, noise, and compression, we introduce adaptive consistency losses imposing consistency between irreversible degradation processes according to the degradation strengths. We demonstrate the effectiveness of BNCR-GAN through large-scale comparative studies on CIFAR-10 and a generality analysis on FFHQ. In addition, we demonstrate the applicability of BNCR-GAN in image restoration. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kaneko_Blur_Noise_and_Compression_Robust_Generative_Adversarial_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.07849 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kaneko_Blur_Noise_and_Compression_Robust_Generative_Adversarial_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kaneko_Blur_Noise_and_Compression_Robust_Generative_Adversarial_Networks_CVPR_2021_paper.html | CVPR 2021 | null | null |
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect | Athena Sayles, Ashish Hooda, Mohit Gupta, Rahul Chatterjee, Earlence Fernandes | Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts -- a sticker on a Stop sign, colorful borders around eyeglasses or a 3D printed object with a colorful texture. An implicit assumption here is that the perturbations must be visible so that a camera can sense them. By contrast, we contribute a procedure to generate, for the first time, physical adversarial examples that are invisible to human eyes. Rather than modifying the victim object with visible artifacts, we modify light that illuminates the object. We demonstrate how an attacker can craft a modulated light signal that adversarially illuminates a scene and causes targeted misclassifications on a state-of-the-art ImageNet deep learning model. Concretely, we exploit the radiometric rolling shutter effect in commodity cameras to create precise striping patterns that appear on images. To human eyes, it appears like the object is illuminated, but the camera creates an image with stripes that will cause ML models to output the attacker-desired classification. We conduct a range of simulation and physical experiments with LEDs, demonstrating targeted attack rates up to 84%. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sayles_Invisible_Perturbations_Physical_Adversarial_Examples_Exploiting_the_Rolling_Shutter_Effect_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sayles_Invisible_Perturbations_Physical_Adversarial_Examples_Exploiting_the_Rolling_Shutter_Effect_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sayles_Invisible_Perturbations_Physical_Adversarial_Examples_Exploiting_the_Rolling_Shutter_Effect_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sayles_Invisible_Perturbations_Physical_CVPR_2021_supplemental.pdf | null |
Introvert: Human Trajectory Prediction via Conditional 3D Attention | Nasim Shafiee, Taskin Padir, Ehsan Elhamifar | Predicting human trajectories is an important component of autonomous moving platforms, such as social robots and self-driving cars. Human trajectories are affected by both the physical features of the environment and social interactions with other humans. Despite recent surge of studies on human path prediction, most works focus on static scene information, therefore, cannot leverage the rich dynamic visual information of the scene. In this work, we propose Introvert, a model which predicts human path based on his/her observed trajectory and the dynamic scene context, captured via a conditional 3D visual attention mechanism working on the input video. Introvert infers both environment constraints and social interactions through observing the dynamic scene instead of communicating with other humans, hence, its computational cost is independent of how crowded the surrounding of a target human is. In addition, to focus on relevant interactions and constraints for each human, Introvert conditions its 3D attention model on the observed trajectory of the target human to extract and focus on relevant spatio-temporal primitives. Our experiments on five publicly available datasets show that the Introvert improves the prediction errors of the state of the art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shafiee_Introvert_Human_Trajectory_Prediction_via_Conditional_3D_Attention_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shafiee_Introvert_Human_Trajectory_Prediction_via_Conditional_3D_Attention_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shafiee_Introvert_Human_Trajectory_Prediction_via_Conditional_3D_Attention_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shafiee_Introvert_Human_Trajectory_CVPR_2021_supplemental.pdf | null |
Camouflaged Object Segmentation With Distraction Mining | Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, Deng-Ping Fan | Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning module (PM) and the focus module (FM). The PM is designed to mimic the detection process in predation for positioning the potential target objects from a global perspective and the FM is then used to perform the identification process in predation for progressively refining the coarse prediction via focusing on the ambiguous regions. Notably, in the FM, we develop a novel distraction mining strategy for the distraction region discovery and removal, to benefit the performance of estimation. Extensive experiments demonstrate that our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging benchmark datasets under four standard metrics. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mei_Camouflaged_Object_Segmentation_With_Distraction_Mining_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10475 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mei_Camouflaged_Object_Segmentation_With_Distraction_Mining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mei_Camouflaged_Object_Segmentation_With_Distraction_Mining_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mei_Camouflaged_Object_Segmentation_CVPR_2021_supplemental.pdf | null |
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction | Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias Niessner | Semantic scene understanding from point clouds is particularly challenging as the points reflect only a sparse set of the underlying 3D geometry. Previous works often convert point cloud into regular grids (e.g. voxels or bird-eye view images), and resort to grid-based convolutions for scene understanding. In this work, we introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from raw point clouds. Instead of representing scenes with regular grids, our method leverages the sparsity of point cloud data and focuses on predicting shapes that are recognized with high objectness. With this design, we decouple the instance reconstruction into global object localization and local shape prediction. It not only eases the difficulty of learning 2-D manifold surfaces from sparse 3D space, the point clouds in each object proposal convey shape details that support implicit function learning to reconstruct any high-resolution surfaces. Our experiments indicate that instance detection and reconstruction present complementary effects, where the shape prediction head shows consistent effects on improving object detection with modern 3D proposal network backbones. The qualitative and quantitative evaluations further demonstrate that our approach consistently outperforms the state-of-the-arts and improves over 11 of mesh IoU in object reconstruction. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nie_RfD-Net_Point_Scene_Understanding_by_Semantic_Instance_Reconstruction_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nie_RfD-Net_Point_Scene_Understanding_by_Semantic_Instance_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nie_RfD-Net_Point_Scene_Understanding_by_Semantic_Instance_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nie_RfD-Net_Point_Scene_CVPR_2021_supplemental.pdf | null |
In the Light of Feature Distributions: Moment Matching for Neural Style Transfer | Nikolai Kalischek, Jan D. Wegner, Konrad Schindler | Style transfer aims to render the content of a given image in the graphical/artistic style of another image. The fundamental concept underlying Neural Style Transfer (NST) is to interpret style as a distribution in the feature space of a Convolutional Neural Network, such that a desired style can be achieved by matching its feature distribution. We show that most current implementations of that concept have important theoretical and practical limitations, as they only partially align the feature distributions. We propose a novel approach that matches the distributions more precisely, thus reproducing the desired style more faithfully, while still being computationally efficient. Specifically, we adapt the dual form of Central Moment Discrepancy, as recently proposed for domain adaptation, to minimize the difference between the target style and the feature distribution of the output image. The dual interpretation of this metric explicitly matches all higher-order centralized moments and is therefore a natural extension of existing NST methods that only take into account the first and second moments. Our experiments confirm that the strong theoretical properties also translate to visually better style transfer, and better disentangle style from semantic image content. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kalischek_In_the_Light_of_Feature_Distributions_Moment_Matching_for_Neural_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07208 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kalischek_In_the_Light_of_Feature_Distributions_Moment_Matching_for_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kalischek_In_the_Light_of_Feature_Distributions_Moment_Matching_for_Neural_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kalischek_In_the_Light_CVPR_2021_supplemental.pdf | null |
DOTS: Decoupling Operation and Topology in Differentiable Architecture Search | Yu-Chao Gu, Li-Juan Wang, Yun Liu, Yi Yang, Yu-Huan Wu, Shao-Ping Lu, Ming-Ming Cheng | Differentiable Architecture Search (DARTS) has attracted extensive attention due to its efficiency in searching for cell structures. DARTS mainly focuses on the operation search and derives the cell topology from the operation weights. However, the operation weights can not indicate the importance of cell topology and result in poor topology rating correctness. To tackle this, we propose to Decouple the Operation and Topology Search (DOTS), which decouples the topology representation from operation weights and makes an explicit topology search. DOTS is achieved by introducing a topology search space that contains combinations of candidate edges. The proposed search space directly reflects the search objective and can be easily extended to support a flexible number of edges in the searched cell. Existing gradient-based NAS methods can be incorporated into DOTS for further improvement by the topology search. Considering that some operations (e.g., Skip-Connection) can affect the topology, we propose a group operation search scheme to preserve topology-related operations for a better topology search. The experiments on CIFAR10/100 and ImageNet demonstrate that DOTS is an effective solution for differentiable NAS. The code is released at https://github.com/guyuchao/DOTS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gu_DOTS_Decoupling_Operation_and_Topology_in_Differentiable_Architecture_Search_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.00969 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gu_DOTS_Decoupling_Operation_and_Topology_in_Differentiable_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gu_DOTS_Decoupling_Operation_and_Topology_in_Differentiable_Architecture_Search_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gu_DOTS_Decoupling_Operation_CVPR_2021_supplemental.pdf | null |
DriveGAN: Towards a Controllable High-Quality Neural Simulation | Seung Wook Kim, Jonah Philion, Antonio Torralba, Sanja Fidler | Realistic simulators are critical for training and verifying robotics systems. While most of the contemporary simulators are hand-crafted, a scaleable way to build simulators is to use machine learning to learn how the environment behaves in response to an action, directly from data. In this work, we aim to learn to simulate a dynamic environment directly in pixel-space, by watching unannotated sequences of frames and their associated action pairs. We introduce a novel high-quality neural simulator referred to as DriveGAN that achieves controllability by disentangling different components without supervision. In addition to steering controls, it also includes controls for sampling features of a scene, such as the weather as well as the location of non-player objects. Since DriveGAN is a fully differentiable simulator, it further allows for re-simulation of a given video sequence, offering an agent to drive through a recorded scene again, possibly taking different actions. We train DriveGAN on multiple datasets, including 160 hours of real-world driving data. We showcase that our approach greatly surpasses the performance of previous data-driven simulators, and allows for new features not explored before. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_DriveGAN_Towards_a_Controllable_High-Quality_Neural_Simulation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.15060 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_DriveGAN_Towards_a_Controllable_High-Quality_Neural_Simulation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_DriveGAN_Towards_a_Controllable_High-Quality_Neural_Simulation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_DriveGAN_Towards_a_CVPR_2021_supplemental.zip | null |
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer | Jiaxin Cheng, Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Prem Natarajan | Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST). Although appealing results have been widely reported in literature, our empirical studies on four well-known AST approaches (GoogleMagenta, AdaIN, LinearTransfer, and SANet) show that more than 50% of the time, AST stylized images are not acceptable to human users, typically due to under- or over-stylization. We systematically study the cause of this imbalanced style transferability (IST) and propose a simple yet effective solution to mitigate this issue. Our studies show that the IST issue is related to the conventional AST style loss, and reveal that the root cause is the equal weightage of training samples irrespective of the properties of their corresponding style images, which biases the model towards certain styles. Through investigation of the theoretical bounds of the AST style loss, we propose a new loss that largely overcomes IST. Theoretical analysis and experimental results validate the effectiveness of our loss, with over 80% relative improvement in style deception rate and 98% relatively higher preference in human evaluation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Style-Aware_Normalized_Loss_for_Improving_Arbitrary_Style_Transfer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10064 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Style-Aware_Normalized_Loss_for_Improving_Arbitrary_Style_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Style-Aware_Normalized_Loss_for_Improving_Arbitrary_Style_Transfer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Style-Aware_Normalized_Loss_CVPR_2021_supplemental.pdf | null |
Wide-Depth-Range 6D Object Pose Estimation in Space | Yinlin Hu, Sebastien Speierer, Wenzel Jakob, Pascal Fua, Mathieu Salzmann | 6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting. One of the most striking differences is the lack of atmospheric scattering, allowing objects to be visible from a great distance while complicating illumination conditions. Currently available benchmark datasets do not place a sufficient emphasis on this aspect and mostly depict the target in close proximity. Prior work tackling pose estimation under large scale variations relies on a two-stage approach to first estimate scale, followed by pose estimation on a resized image patch. We instead propose a single-stage hierarchical end-to-end trainable network that is more robust to scale variations. We demonstrate that it outperforms existing approaches not only on images synthesized to resemble images taken in space but also on standard benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Wide-Depth-Range_6D_Object_Pose_Estimation_in_Space_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00337 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Wide-Depth-Range_6D_Object_Pose_Estimation_in_Space_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Wide-Depth-Range_6D_Object_Pose_Estimation_in_Space_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Salient Boundary Feature for Anchor-free Temporal Action Localization | Chuming Lin, Chengming Xu, Donghao Luo, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Yanwei Fu | Temporal action localization is an important yet challenging task in video understanding. Typically, such a task aims at inferring both the action category and localization of the start and end frame for each action instance in a long, untrimmed video. While most current models achieve good results by using pre-defined anchors and numerous actionness, such methods could be bothered with both large number of outputs and heavy tuning of locations and sizes corresponding to different anchors. Instead, anchor-free methods is lighter, getting rid of redundant hyper-parameters, but gains few attention. In this paper, we propose the first purely anchor-free temporal localization method, which is both efficient and effective. Our model includes (i) an end-to-end trainable basic predictor, (ii) a saliency-based refinement module to gather more valuable boundary features for each proposal with a novel boundary pooling, and (iii) several consistency constraints to make sure our model can find the accurate boundary given arbitrary proposals. Extensive experiments show that our method beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3. Our code will be made available upon publication. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Learning_Salient_Boundary_Feature_for_Anchor-free_Temporal_Action_Localization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Learning_Salient_Boundary_Feature_for_Anchor-free_Temporal_Action_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Learning_Salient_Boundary_Feature_for_Anchor-free_Temporal_Action_Localization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Learning_Salient_Boundary_CVPR_2021_supplemental.pdf | null |
Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model | Julian Lienen, Eyke Hullermeier, Ralph Ewerth, Nils Nommensen | In many real-world applications, the relative depth of objects in an image is crucial for scene understanding. Recent approaches mainly tackle the problem of depth prediction in monocular images by treating the problem as a regression task. Yet, being interested in an order relation in the first place, ranking methods suggest themselves as a natural alternative to regression, and indeed, ranking approaches leveraging pairwise comparisons as training information ("object A is closer to the camera than B") have shown promising performance on this problem. In this paper, we elaborate on the use of so-called listwise ranking as a generalization of the pairwise approach. Our method is based on the Plackett-Luce (PL) model, a probability distribution on rankings, which we combine with a state-of-the-art neural network architecture and a simple sampling strategy to reduce training complexity. Moreover, taking advantage of the representation of PL as a random utility model, the proposed predictor offers a natural way to recover (shift-invariant) metric depth information from ranking-only data provided at training time. An empirical evaluation on several benchmark datasets in a "zero-shot" setting demonstrates the effectiveness of our approach compared to existing ranking and regression methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lienen_Monocular_Depth_Estimation_via_Listwise_Ranking_Using_the_Plackett-Luce_Model_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lienen_Monocular_Depth_Estimation_via_Listwise_Ranking_Using_the_Plackett-Luce_Model_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lienen_Monocular_Depth_Estimation_via_Listwise_Ranking_Using_the_Plackett-Luce_Model_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lienen_Monocular_Depth_Estimation_CVPR_2021_supplemental.pdf | null |
Holistic 3D Scene Understanding From a Single Image With Implicit Representation | Cheng Zhang, Zhaopeng Cui, Yinda Zhang, Bing Zeng, Marc Pollefeys, Shuaicheng Liu | We present a new pipeline for holistic 3D scene understanding from a single image, which could predict object shape, object pose and scene layout. As it is a highly ill-posed problem, existing methods usually suffer from inaccurate estimation of both shapes and layout especially for the cluttered scene due to the heavy occlusion between objects. We propose to utilize the latest deep implicit representation to solve this challenge. We not only propose an image-based local structured implicit network to improve the object shape estimation, but also refine 3D object pose and scene layout via an novel implicit scene graph neural network that exploits the implicit local object features. A novel physical violation loss is also proposed to avoid incorrect context between objects. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of object shape, scene layout estimation, and 3D object detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Holistic_3D_Scene_Understanding_From_a_Single_Image_With_Implicit_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06422 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Holistic_3D_Scene_Understanding_From_a_Single_Image_With_Implicit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Holistic_3D_Scene_Understanding_From_a_Single_Image_With_Implicit_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Holistic_3D_Scene_CVPR_2021_supplemental.pdf | null |
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization | Jiahui Huang, He Wang, Tolga Birdal, Minhyuk Sung, Federica Arrigoni, Shi-Min Hu, Leonidas J. Guibas | We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting that we investigate are: (i) guaranteeing correspondence and segmentation consistency across multiple input point clouds capturing different spatial arrangements of bodies or body parts; and (ii) obtaining robust motion-based rigid body segmentation applicable to novel object categories. We propose an approach to address these issues that incorporates spectral synchronization into an iterative deep declarative network, so as to simultaneously recover consistent correspondences as well as motion segmentation. At the same time, by explicitly disentangling the correspondence and motion segmentation estimation modules, we achieve strong generalizability across different object categories. Our extensive evaluations demonstrate that our method is effective on various datasets ranging from rigid parts in articulated objects to individually moving objects in a 3D scene, be it single-view or full point clouds. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_MultiBodySync_Multi-Body_Segmentation_and_Motion_Estimation_via_3D_Scan_Synchronization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.06605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MultiBodySync_Multi-Body_Segmentation_and_Motion_Estimation_via_3D_Scan_Synchronization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MultiBodySync_Multi-Body_Segmentation_and_Motion_Estimation_via_3D_Scan_Synchronization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_MultiBodySync_Multi-Body_Segmentation_CVPR_2021_supplemental.pdf | null |
Learning Optical Flow From a Few Matches | Shihao Jiang, Yao Lu, Hongdong Li, Richard Hartley | State-of-the-art neural network models for optical flow estimation require a dense correlation volume at high resolutions for representing per-pixel displacement. Although the dense correlation volume is informative for accurate estimation, its heavy computation and memory usage hinders the efficient training and deployment of the models. In this paper, we show that the dense correlation volume representation is redundant and accurate flow estimation can be achieved with only a fraction of elements in it. Based on this observation, we propose an alternative displacement representation, named Sparse Correlation Volume, which is constructed directly by computing the k closest matches in one feature map for each feature vector in the other feature map and stored in a sparse data structure. Experiments show that our method can reduce computational cost and memory use significantly and produce fine-structure motion, while maintaining high accuracy compared to previous approaches with dense correlation volumes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiang_Learning_Optical_Flow_From_a_Few_Matches_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02166 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Learning_Optical_Flow_From_a_Few_Matches_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Learning_Optical_Flow_From_a_Few_Matches_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learnable Motion Coherence for Correspondence Pruning | Yuan Liu, Lingjie Liu, Cheng Lin, Zhen Dong, Wenping Wang | Motion coherence is an important clue for distinguishing true correspondences from false ones. Modeling motion coherence on sparse putative correspondences is challenging due to their sparsity and uneven distributions. Existing works on motion coherence are sensitive to parameter settings and have difficulty in dealing with complex motion patterns. In this paper, we introduce a network called Laplacian Motion Coherence Network (LMCNet) to learn motion coherence property for correspondence pruning. We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian. This closed-form solution enables us to design a differentiable layer in a learning framework to capture global motion coherence from putative correspondences. The global motion coherence is further combined with local coherence extracted by another local layer to robustly detect inlier correspondences. Experiments demonstrate that LMCNet has superior performances to the state of the art in relative camera pose estimation and correspondences pruning of dynamic scenes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Learnable_Motion_Coherence_for_Correspondence_Pruning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14563 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Learnable_Motion_Coherence_for_Correspondence_Pruning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Learnable_Motion_Coherence_for_Correspondence_Pruning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Learnable_Motion_Coherence_CVPR_2021_supplemental.pdf | null |
ManipulaTHOR: A Framework for Visual Object Manipulation | Kiana Ehsani, Winson Han, Alvaro Herrasti, Eli VanderBilt, Luca Weihs, Eric Kolve, Aniruddha Kembhavi, Roozbeh Mottaghi | The domain of Embodied AI has recently witnessed substantial progress, particularly in navigating agents within their environments. These early successes have laid the building blocks for the community to tackle tasks that require agents to actively interact with objects in their environment. Object manipulation is an established research domain within the robotics community and poses several challenges including manipulator motion, grasping and long-horizon planning, particularly when dealing with oft-overlooked practical setups involving visually rich and complex scenes, manipulation using mobile agents (as opposed to tabletop manipulation), and generalization to unseen environments and objects. We propose a framework for object manipulation built upon the physics-enabled, visually rich AI2-THOR framework and present a new challenge to the Embodied AI community known as ArmPointNav. This task extends the popular point navigation task to object manipulation and offers new challenges including 3D obstacle avoidance, manipulating objects in the presence of occlusion, and multi-object manipulation that necessitates long term planning. Popular learning paradigms that are successful on PointNav challenges show promise, but leave a large room for improvement. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ehsani_ManipulaTHOR_A_Framework_for_Visual_Object_Manipulation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11213 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ehsani_ManipulaTHOR_A_Framework_for_Visual_Object_Manipulation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ehsani_ManipulaTHOR_A_Framework_for_Visual_Object_Manipulation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ehsani_ManipulaTHOR_A_Framework_CVPR_2021_supplemental.zip | null |
DeepI2P: Image-to-Point Cloud Registration via Deep Classification | Jiaxin Li, Gim Hee Lee | This paper presents DeepI2P: a novel approach for cross-modality registration between an image and a point cloud. Given an image (e.g. from a rgb-camera) and a general point cloud (e.g. from a 3D Lidar scanner) captured at different locations in the same scene, our method estimates the relative rigid transformation between the coordinate frames of the camera and Lidar. Learning common feature descriptors to establish correspondences for the registration is inherently challenging due to the lack of appearance and geometric correlations across the two modalities. We circumvent the difficulty by converting the registration problem into a classification and inverse camera projection optimization problem. A classification neural network is designed to label whether the projection of each point in the point cloud is within or beyond the camera frustum. These labeled points are subsequently passed into a novel inverse camera projection solver to estimate the relative pose. Extensive experimental results on Oxford Robotcar and KITTI datasets demonstrate the feasibility of our approach. Our source code is available at https://github.com/lijx10/DeepI2P | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03501 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_DeepI2P_Image-to-Point_Cloud_CVPR_2021_supplemental.pdf | null |
Scene-Intuitive Agent for Remote Embodied Visual Grounding | Xiangru Lin, Guanbin Li, Yizhou Yu | Humans learn from life events to form intuitions towards the understanding of visual environments and languages. Envision that you are instructed by a high-level instruction, "Go to the bathroom in the master bedroom and replace the blue towel on the left wall", what would you possibly do to carry out the task? Intuitively, we comprehend the semantics of the instruction to form an overview of where a bathroom is and what a blue towel is in mind; then, we navigate to the target location by consistently matching the bathroom appearance in mind with the current scene. In this paper, we present an agent that mimics such human behaviors. Specifically, we focus on the Remote Embodied Visual Referring Expression in Real Indoor Environments task, called REVERIE, where an agent is asked to correctly localize a remote target object specified by a concise high-level natural language instruction, and propose a two-stage training pipeline. In the first stage, we pre-train the agent with two cross-modal alignment sub-tasks, namely the Scene Grounding task and the Object Grounding task. The agent learns where to stop in the Scene Grounding task and what to attend to in the Object Grounding task respectively. Then, to generate action sequences, we propose a memory-augmented attentive action decoder to smoothly fuse the pre-trained vision and language representations with the agent's past memory experiences. Without bells and whistles, experimental results show that our method outperforms previous state-of-the-art(SOTA) significantly, demonstrating the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Scene-Intuitive_Agent_for_Remote_Embodied_Visual_Grounding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Scene-Intuitive_Agent_for_Remote_Embodied_Visual_Grounding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Scene-Intuitive_Agent_for_Remote_Embodied_Visual_Grounding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Scene-Intuitive_Agent_for_CVPR_2021_supplemental.pdf | null |
Human-Like Controllable Image Captioning With Verb-Specific Semantic Roles | Long Chen, Zhihong Jiang, Jun Xiao, Wei Liu | Controllable Image Captioning (CIC) -- generating image descriptions following designated control signals -- has received unprecedented attention over the last few years. To emulate the human ability in controlling caption generation, current CIC studies focus exclusively on control signals concerning objective properties, such as contents of interest or descriptive patterns. However, we argue that almost all existing objective control signals have overlooked two indispensable characteristics of an ideal control signal: 1) Event-compatible: all visual contents referred to in a single sentence should be compatible with the described activity. 2) Sample-suitable: the control signals should be suitable for a specific image sample. To this end, we propose a new control signal for CIC: Verb-specific Semantic Roles (VSR). VSR consists of a verb and some semantic roles, which represents a targeted activity and the roles of entities involved in this activity. Given a designated VSR, we first train a grounded semantic role labeling (GSRL) model to identify and ground all entities for each role. Then, we propose a semantic structure planner (SSP) to learn human-like descriptive semantic structures. Lastly, we use a role-shift captioning model to generate the captions. Extensive experiments and ablations demonstrate that our framework can achieve better controllability than several strong baselines on two challenging CIC benchmarks. Besides, we can generate multi-level diverse captions easily. The code is available at: https://github.com/mad-red/VSR-guided-CIC. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Human-Like_Controllable_Image_Captioning_With_Verb-Specific_Semantic_Roles_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12204 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Human-Like_Controllable_Image_Captioning_With_Verb-Specific_Semantic_Roles_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Human-Like_Controllable_Image_Captioning_With_Verb-Specific_Semantic_Roles_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Human-Like_Controllable_Image_CVPR_2021_supplemental.pdf | null |
Enhancing the Transferability of Adversarial Attacks Through Variance Tuning | Xiaosen Wang, Kun He | Deep neural networks are vulnerable to adversarial examples that mislead the models with imperceptible perturbations. Though adversarial attacks have achieved incredible success rates in the white-box setting, most existing adversaries often exhibit weak transferability in the black-box setting, especially under the scenario of attacking models with defense mechanisms. In this work, we propose a new method called variance tuning to enhance the class of iterative gradient based attack methods and improve their attack transferability. Specifically, at each iteration for the gradient calculation, instead of directly using the current gradient for the momentum accumulation, we further consider the gradient variance of the previous iteration to tune the current gradient so as to stabilize the update direction and escape from poor local optima. Empirical results on the standard ImageNet dataset demonstrate that our method could significantly improve the transferability of gradient-based adversarial attacks. Besides, our method could be used to attack ensemble models or be integrated with various input transformations. Incorporating variance tuning with input transformations on iterative gradient-based attacks in the multi-model setting, the integrated method could achieve an average success rate of 90.1% against nine advanced defense methods, improving the current best attack performance significantly by 85.1%. Code is available at https://github.com/JHL-HUST/VT. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Enhancing_the_Transferability_of_Adversarial_Attacks_Through_Variance_Tuning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15571 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Enhancing_the_Transferability_of_Adversarial_Attacks_Through_Variance_Tuning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Enhancing_the_Transferability_of_Adversarial_Attacks_Through_Variance_Tuning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Enhancing_the_Transferability_CVPR_2021_supplemental.pdf | null |
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms | Mahmoud Afifi, Marcus A. Brubaker, Michael S. Brown | While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. Simplifying GAN-based image generation is critical for their adoption in graphic design and artistic work. This goal has led to significant interest in methods that can intuitively control the appearance of images generated by GANs. In this paper, we present HistoGAN, a color histogram-based method for controlling GAN-generated images' colors. We focus on color histograms as they provide an intuitive way to describe image color while remaining decoupled from domain-specific semantics. Specifically, we introduce an effective modification of the recent StyleGAN architecture [31] to control the colors of GAN-generated images specified by a target color histogram feature. We then describe how to expand HistoGAN to recolor real images. For image recoloring, we jointly train an encoder network along with HistoGAN. The recoloring model, ReHistoGAN, is an unsupervised approach trained to encourage the network to keep the original image's content while changing the colors based on the given target histogram. We show that this histogram-based approach offers a better way to control GAN-generated and real images' colors while producing more compelling results compared to existing alternative strategies. | https://openaccess.thecvf.com/content/CVPR2021/papers/Afifi_HistoGAN_Controlling_Colors_of_GAN-Generated_and_Real_Images_via_Color_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11731 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Afifi_HistoGAN_Controlling_Colors_of_GAN-Generated_and_Real_Images_via_Color_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Afifi_HistoGAN_Controlling_Colors_of_GAN-Generated_and_Real_Images_via_Color_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Afifi_HistoGAN_Controlling_Colors_CVPR_2021_supplemental.pdf | null |
BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification | Ruibing Hou, Hong Chang, Bingpeng Ma, Rui Huang, Shiguang Shan | In this paper, we present an efficient spatial-temporal representation for video person re-identification (reID). Firstly, we propose a Bilateral Complementary Network (BiCnet) for spatial complementarity modeling. Specifically, BiCnet contains two branches. Detail Branch processes frames at original resolution to preserve the detailed visual clues, and Context Branch with a down-sampling strategy is employed to capture long-range contexts. On each branch, BiCnet appends multiple parallel and diverse attention modules to discover divergent body parts for consecutive frames, so as to obtain an integral characteristic of target identity. Furthermore, a Temporal Kernel Selection (TKS) block is designed to capture short-term as well as long-term temporal relations by an adaptive mode. TKS can be inserted into BiCnet at any depth to construct BiCnet-TKS for spatial-temporal modeling. Experimental results on multiple benchmarks show that BiCnet-TKS outperforms state-of-the-arts with about 50% less computations. The source code is available at https://github.com/blue-blue272/BiCnet-TKS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_BiCnet-TKS_Learning_Efficient_Spatial-Temporal_Representation_for_Video_Person_Re-Identification_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_BiCnet-TKS_Learning_Efficient_Spatial-Temporal_Representation_for_Video_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_BiCnet-TKS_Learning_Efficient_Spatial-Temporal_Representation_for_Video_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | null | null |
Probabilistic Model Distillation for Semantic Correspondence | Xin Li, Deng-Ping Fan, Fan Yang, Ao Luo, Hong Cheng, Zicheng Liu | Semantic correspondence is a fundamental problem in computer vision, which aims at establishing dense correspondences across images depicting different instances under the same category. This task is challenging due to large intra-class variations and a severe lack of ground truth. A popular solution is to learn correspondences from synthetic data. However, because of the limited intra-class appearance and background variations within synthetically generated training data, the model's capability for handling "real" image pairs using such strategy is intrinsically constrained. We address this problem with the use of a novel Probabilistic Model Distillation (PMD) approach which transfers knowledge learned by a probabilistic teacher model on synthetic data to a static student model with the use of unlabeled real image pairs. A probabilistic supervision reweighting (PSR) module together with a confidence-aware loss (CAL) is used to mine the useful knowledge and alleviate the impact of errors. Experimental results on a variety of benchmarks show that our PMD achieves state-of-the-art performance. To demonstrate the generalizability of our approach, we extend PMD to incorporate stronger supervision for better accuracy -- the probabilistic teacher is trained with stronger key-point supervision. Again, we observe the superiority of our PMD. The extensive experiments verify that PMD is able to infer more reliable supervision signals from the probabilistic teacher for representation learning and largely alleviate the influence of errors in pseudo labels. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Probabilistic_Model_Distillation_for_Semantic_Correspondence_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Probabilistic_Model_Distillation_for_Semantic_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Probabilistic_Model_Distillation_for_Semantic_Correspondence_CVPR_2021_paper.html | CVPR 2021 | null | null |
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets | Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Meng Song, Yuhan Liu, Yu-Ying Yeh, Rui Zhu, Nitesh Gundavarapu, Jia Shi, Sai Bi, Hong-Xing Yu, Zexiang Xu, Kalyan Sunkavalli, Milos Hasan, Ravi Ramamoorthi, Manmohan Chandraker | We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make the dataset creation process widely accessible, allowing researchers to transform scans into datasets with highquality ground truth. We demonstrate our framework by creating a photorealistic synthetic version of the publicly available ScanNet dataset with consistent layout, semantic labels, high quality spatially-varying BRDF and complex lighting. We render photorealistic images, as well as complex spatially-varying lighting, including direct, indirect and visibility components. Such a dataset enables important applications in inverse rendering, scene understanding and robotics. We show that deep networks trained on the proposed dataset achieve competitive performance for shape, material and lighting estimation on real images, enabling photorealistic augmented reality applications, such as object insertion and material editing. We also show our semantic labels may be used for segmentation and multitask learning. Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes. The dataset and all the tools to create such datasets will be publicly released, enabling others in the community to easily build large-scale datasets of their own. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_OpenRooms_An_Open_Framework_for_Photorealistic_Indoor_Scene_Datasets_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_OpenRooms_An_Open_Framework_for_Photorealistic_Indoor_Scene_Datasets_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_OpenRooms_An_Open_Framework_for_Photorealistic_Indoor_Scene_Datasets_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_OpenRooms_An_Open_CVPR_2021_supplemental.pdf | null |
SSAN: Separable Self-Attention Network for Video Representation Learning | Xudong Guo, Xun Guo, Yan Lu | Self-attention has been successfully applied to video representation learning due to the effectiveness of modeling long range dependencies. Existing approaches build the dependencies merely by computing the pairwise correlations along spatial and temporal dimensions simultaneously. However, spatial correlations and temporal correlations represent different contextual information of scenes and temporal reasoning. Intuitively, learning spatial contextual information first will benefit temporal modeling. In this paper, we propose a separable self-attention (SSA) module, which models spatial and temporal correlations sequentially, so that spatial contexts can be efficiently used in temporal modeling. By adding SSA module into 2D CNN, we build a SSA network (SSAN) for video representation learning. On the task of video action recognition, our approach outperforms state-of-the-art methods on Something-Something and Kinetics-400 datasets. Our models often outperform counterparts with shallower network and less modality. We further verify the semantic learning ability of our method in visual-language task of video retrieval, which showcase the homogeneity of video representations and text embeddings. On MSR-VTT and Youcook2 datasets, video representations learnt by SSA significantly improve the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_SSAN_Separable_Self-Attention_Network_for_Video_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.13033 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_SSAN_Separable_Self-Attention_Network_for_Video_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_SSAN_Separable_Self-Attention_Network_for_Video_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
4D Panoptic LiDAR Segmentation | Mehmet Aygun, Aljosa Osep, Mark Weber, Maxim Maximov, Cyrill Stachniss, Jens Behley, Laura Leal-Taixe | Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points. To this end, we present an approach and a novel evaluation metric. Our approach determines a semantic class for every point while modeling object instances as probability distributions in the 4D spatio-temporal domain. We process multiple point clouds in parallel and resolve point-to-instance associations, effectively alleviating the need for explicit temporal data association. Inspired by recent advances in benchmarking of multi-object tracking, we propose to adopt a new evaluation metric that separates the semantic and point-to-instance association aspects of the task. With this work, we aim at paving the road for future developments aiming at temporal LiDAR panoptic perception. | https://openaccess.thecvf.com/content/CVPR2021/papers/Aygun_4D_Panoptic_LiDAR_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.12472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Aygun_4D_Panoptic_LiDAR_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Aygun_4D_Panoptic_LiDAR_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Aygun_4D_Panoptic_LiDAR_CVPR_2021_supplemental.pdf | null |
SceneGen: Learning To Generate Realistic Traffic Scenes | Shuhan Tan, Kelvin Wong, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun | We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones. As a result, existing simulators lack the fidelity necessary to train and test self-driving vehicles. To address this limitation, we present SceneGen, a neural autoregressive model of traffic scenes that eschews the need for rules and heuristics. In particular, given the ego-vehicle state and a high definition map of surrounding area, SceneGen inserts actors of various classes into the scene and synthesizes their sizes, orientations, and velocities. We demonstrate on two large-scale datasets SceneGen's ability to faithfully model distributions of real traffic scenes. Moreover, we show that SceneGen coupled with sensor simulation can be used to train perception models that generalize to the real world. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_SceneGen_Learning_To_Generate_Realistic_Traffic_Scenes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.06541 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_SceneGen_Learning_To_Generate_Realistic_Traffic_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_SceneGen_Learning_To_Generate_Realistic_Traffic_Scenes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_SceneGen_Learning_To_CVPR_2021_supplemental.pdf | null |
Natural Adversarial Examples | Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song | We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets' real-world, unmodified examples transfer to various unseen models reliably, demonstrating that computer vision models have shared weaknesses. The first dataset is called ImageNet-A and is like the ImageNet test set, but it is far more challenging for existing models. We also curate an adversarial out-of-distribution detection dataset called ImageNet-O, which is the first out-of-distribution detection dataset created for ImageNet models. On ImageNet-A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%, and its out-of-distribution detection performance on ImageNet-O is near random chance levels. We find that existing data augmentation techniques hardly boost performance, and using other public training datasets provides improvements that are limited. However, we find that improvements to computer vision architectures provide a promising path towards robust models. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hendrycks_Natural_Adversarial_Examples_CVPR_2021_paper.pdf | http://arxiv.org/abs/1907.07174 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hendrycks_Natural_Adversarial_Examples_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hendrycks_Natural_Adversarial_Examples_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hendrycks_Natural_Adversarial_Examples_CVPR_2021_supplemental.pdf | null |
CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models | Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, Jun Wang | Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. However, in real scenarios, factors with semantics are not necessarily independent. Instead, there might be an underlying causal structure which renders these factors dependent. We thus propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent exogenous factors into causal endogenous ones that correspond to causally related concepts in data. We further analyze the model identifiabitily, showing that the proposed model learned from observations recovers the true one up to a certain degree. Experiments are conducted on various datasets, including synthetic and real word benchmark CelebA. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we demonstrate that the proposed CausalVAE model is able to generate counterfactual data through "do-operation" to the causal factors. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_CausalVAE_Disentangled_Representation_Learning_via_Neural_Structural_Causal_Models_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.08697 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_CausalVAE_Disentangled_Representation_Learning_via_Neural_Structural_Causal_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_CausalVAE_Disentangled_Representation_Learning_via_Neural_Structural_Causal_Models_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_CausalVAE_Disentangled_Representation_CVPR_2021_supplemental.pdf | null |
VideoMoCo: Contrastive Video Representation Learning With Temporally Adversarial Examples | Tian Pan, Yibing Song, Tianyu Yang, Wenhao Jiang, Wei Liu | MoCo is effective for unsupervised image representation learning. In this paper, we propose VideoMoCo for unsupervised video representation learning. Given a video sequence as an input sample, we improve the temporal feature representations of MoCo from two perspectives. First, we introduce a generator to drop out several frames from this sample temporally. The discriminator is then learned to encode similar feature representations regardless of frame removals. By adaptively dropping out different frames during training iterations of adversarial learning, we augment this input sample to train a temporally robust encoder. Second, we use temporal decay to model key attenuation in the memory queue when computing the contrastive loss. As the momentum encoder updates after keys enqueue, the representation ability of these keys degrades when we use the current input sample for contrastive learning. This degradation is reflected via temporal decay to attend the input sample to recent keys in the queue. As a result, we adapt MoCo to learn video representations without empirically designing pretext tasks. By empowering the temporal robustness of the encoder and modeling the temporal decay of the keys, our VideoMoCo improves MoCo temporally based on contrastive learning. Experiments on benchmark datasets including UCF101 and HMDB51 show that VideoMoCo stands as a state-of-the-art video representation learning method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_VideoMoCo_Contrastive_Video_Representation_Learning_With_Temporally_Adversarial_Examples_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05905 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_VideoMoCo_Contrastive_Video_Representation_Learning_With_Temporally_Adversarial_Examples_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_VideoMoCo_Contrastive_Video_Representation_Learning_With_Temporally_Adversarial_Examples_CVPR_2021_paper.html | CVPR 2021 | null | null |
Zero-Shot Instance Segmentation | Ye Zheng, Jiahong Wu, Yongqiang Qin, Faen Zhang, Li Cui | Deep learning has significantly improved the precision of instance segmentation with abundant labeled data. However, in many areas like medical and manufacturing, collecting sufficient data is extremely hard and labeling this data requires high professional skills. We follow this motivation and propose a new task set named zero-shot instance segmentation (ZSI). In the training phase of ZSI, the model is trained with seen data, while in the testing phase, it is used to segment all seen and unseen instances. We first formulate the ZSI task and propose a method to tackle the challenge, which consists of Zero-shot Detector, Semantic Mask Head, Background Aware RPN and Synchronized Background Strategy. We present a new benchmark for zero-shot instance segmentation based on the MS-COCO dataset. The extensive empirical results in this benchmark show that our method not only surpasses the state-of-the-art results in zero-shot object detection task but also achieves promising performance on ZSI. Our approach will serve as a solid baseline and facilitate future research in zero-shot instance segmentation. Code available at ZSI. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Zero-Shot_Instance_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06601 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Zero-Shot_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Zero-Shot_Instance_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Zero-Shot_Instance_Segmentation_CVPR_2021_supplemental.pdf | null |
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes | Julian Chibane, Aayush Bansal, Verica Lazova, Gerard Pons-Moll | Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction. State-of-the-Art methods, such as NeRF, are designed to learn a single scene with a neural network and require dense multi-view inputs. Testing on a new scene requires re-training from scratch, which takes 2-3 days. In this work, we introduce Stereo Radiance Fields (SRF), a neural view synthesis approach that is trained end-to-end, generalizes to new scenes, and requires only sparse views at test time. The core idea is a neural architecture inspired by classical multi-view stereo methods, which estimates surface points by finding similar image regions in stereo images. In SRF, we predict color and density for each 3D point given an encoding of its stereo correspondence in the input images. The encoding is implicitly learned by an ensemble of pair-wise similarities -- emulating classical stereo. Experiments show that SRF learns structure instead of overfitting on a scene. We train on multiple scenes of the DTU dataset and generalize to new ones without re-training, requiring only 10 sparse and spread-out views as input. We show that 10-15 minutes of fine-tuning further improve the results, achieving significantly sharper, more detailed results than scene-specific models. The code, model, and videos are available at https://virtualhumans.mpi-inf.mpg.de/srf/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chibane_Stereo_Radiance_Fields_SRF_Learning_View_Synthesis_for_Sparse_Views_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06935 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chibane_Stereo_Radiance_Fields_SRF_Learning_View_Synthesis_for_Sparse_Views_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chibane_Stereo_Radiance_Fields_SRF_Learning_View_Synthesis_for_Sparse_Views_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chibane_Stereo_Radiance_Fields_CVPR_2021_supplemental.pdf | null |
Global Transport for Fluid Reconstruction With Learned Self-Supervision | Erik Franz, Barbara Solenthaler, Nils Thuerey | We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation. Instead of obtaining the space-time function of the observations, we reconstruct its motion based on a single initial state. In addition we introduce a learned self-supervision that constrains observations from unseen angles. These visual constraints are coupled via the transport constraints and a differentiable rendering step to arrive at a robust end-to-end reconstruction algorithm. This makes the reconstruction of highly realistic flow motions possible, even from only a single input view. We show with a variety of synthetic and real flows that the proposed global reconstruction of the transport process yields an improved reconstruction of the fluid motion. | https://openaccess.thecvf.com/content/CVPR2021/papers/Franz_Global_Transport_for_Fluid_Reconstruction_With_Learned_Self-Supervision_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06031 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Franz_Global_Transport_for_Fluid_Reconstruction_With_Learned_Self-Supervision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Franz_Global_Transport_for_Fluid_Reconstruction_With_Learned_Self-Supervision_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Franz_Global_Transport_for_CVPR_2021_supplemental.pdf | null |
SliceNet: Deep Dense Depth Estimation From a Single Indoor Panorama Using a Slice-Based Representation | Giovanni Pintore, Marco Agus, Eva Almansa, Jens Schneider, Enrico Gobbetti | We introduce a novel deep neural network to estimate a depth map from a single monocular indoor panorama. The network directly works on the equirectangular projection, exploiting the properties of indoor 360 images. Starting from the fact that gravity plays an important role in the design and construction of man-made indoor scenes, we propose a compact representation of the scene into vertical slices of the sphere, and we exploit long- and short-term relationships among slices to recover the equirectangular depth map. Our design makes it possible to maintain high-resolution information in the extracted features even with a deep network. The experimental results demonstrate that our method outperforms current state-of-the-art solutions in prediction accuracy, particularly for real-world data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pintore_SliceNet_Deep_Dense_Depth_Estimation_From_a_Single_Indoor_Panorama_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pintore_SliceNet_Deep_Dense_Depth_Estimation_From_a_Single_Indoor_Panorama_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pintore_SliceNet_Deep_Dense_Depth_Estimation_From_a_Single_Indoor_Panorama_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pintore_SliceNet_Deep_Dense_CVPR_2021_supplemental.pdf | null |
Offboard 3D Object Detection From Point Cloud Sequences | Charles R. Qi, Yin Zhou, Mahyar Najibi, Pei Sun, Khoa Vo, Boyang Deng, Dragomir Anguelov | While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels. Existing 3D object detectors fail to satisfy the high-quality requirement for offboard uses due to the limited input and speed constraints. In this paper, we propose a novel offboard 3D object detection pipeline using point cloud sequence data. Observing that different frames capture complementary views of objects, we design the offboard detector to make use of the temporal points through both multi-frame object detection and novel object-centric refinement models. Evaluated on the Waymo Open Dataset, our pipeline named 3D Auto Labeling shows significant gains compared to the state-of-the-art onboard detectors and our offboard baselines. Its performance is even on par with human labels verified through a human label study. Further experiments demonstrate the application of auto labels for semi-supervised learning and provide extensive analysis to validate various design choices. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qi_Offboard_3D_Object_Detection_From_Point_Cloud_Sequences_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05073 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qi_Offboard_3D_Object_Detection_From_Point_Cloud_Sequences_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qi_Offboard_3D_Object_Detection_From_Point_Cloud_Sequences_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qi_Offboard_3D_Object_CVPR_2021_supplemental.zip | null |
STaR: Self-Supervised Tracking and Reconstruction of Rigid Objects in Motion With Neural Rendering | Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, Steven Lovegrove | We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are surprisingly effective at the task of compressing many views of a scene into a learned function which maps from a viewing ray to an observed radiance value via volume rendering. Unfortunately, these methods lose all their predictive power once any object in the scene has moved. In this work, we explicitly model rigid motion of objects in the context of neural representations of radiance fields. We show that without any additional human specified supervision, we can reconstruct a dynamic scene with a single rigid object in motion by simultaneously decomposing it into its two constituent parts and encoding each with its own neural representation. We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame. On both synthetic and real world datasets, we demonstrate that our method can render photorealistic novel views, where novelty is measured on both spatial and temporal axes. Our factored representation furthermore enables animation of unseen object motion. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_STaR_Self-Supervised_Tracking_and_Reconstruction_of_Rigid_Objects_in_Motion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.01602 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_STaR_Self-Supervised_Tracking_and_Reconstruction_of_Rigid_Objects_in_Motion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_STaR_Self-Supervised_Tracking_and_Reconstruction_of_Rigid_Objects_in_Motion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yuan_STaR_Self-Supervised_Tracking_CVPR_2021_supplemental.zip | null |
Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections | Prashant Pandey, Mrigank Raman, Sumanth Varambally, Prathosh AP | Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem. While many approaches have been proposed to solve this problem, they only utilize source data during training, but do not take advantage of the fact that a single target example is available at the time of inference. Motivated by this, we propose a method that effectively uses the target sample during inference beyond mere classification. Our method has three components - (i) A label preserving feature or metric transformation on source data such that the source samples are clustered in accordance with their class irrespective of their domain (ii) A generative model trained on the these features (iii) A label-preserving projection of the target point on the source-feature manifold during inference via solving an optimization problem on the input space of the generative model using the learned metric. Finally, the projected target is used in the classifier. Since the projected target feature comes from the source manifold and has the same label as the real target by design, the classifier is expected to perform better on it than the true target. We demonstrate that our method outperforms the state-of-the-art Domain Generalization methods on multiple datasets and tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pandey_Generalization_on_Unseen_Domains_via_Inference-Time_Label-Preserving_Target_Projections_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pandey_Generalization_on_Unseen_Domains_via_Inference-Time_Label-Preserving_Target_Projections_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pandey_Generalization_on_Unseen_Domains_via_Inference-Time_Label-Preserving_Target_Projections_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pandey_Generalization_on_Unseen_CVPR_2021_supplemental.pdf | null |
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach | Yunsong Zhou, Yuan He, Hongzi Zhu, Cheng Wang, Hongyang Li, Qinhong Jiang | Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r.t. ground plane. This is common due to the slight fluctuation of road smoothness and slope. Due to the lack of insight in industrial application, existing methods on open datasets neglect camera pose information, which inevitably results in the detector being susceptible to camera extrinsic parameters. The perturbation of objects is very popular in most autonomous driving cases for industrial products. To this end, we propose a novel method to capture camera pose to formulate the detector free from extrinsic perturbation. Specifically, the proposed framework predicts camera extrinsic parameters by detecting vanishing point and horizon change. A converter is designed to rectify perturbative features in the latent space. By doing so, our 3D detector works independent of the extrinsic parameter variations, and produces accurate results in realistic cases, e.g., potholed and uneven roads, where almost all existing monocular detectors fail to handle. Experiments demonstrate our method yields best performance compared with the other state-of-the-arts by a large margin on both KITTI 3D and nuScenes datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Monocular_3D_Object_Detection_An_Extrinsic_Parameter_Free_Approach_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Monocular_3D_Object_Detection_An_Extrinsic_Parameter_Free_Approach_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Monocular_3D_Object_Detection_An_Extrinsic_Parameter_Free_Approach_CVPR_2021_paper.html | CVPR 2021 | null | null |
Communication Efficient SGD via Gradient Sampling With Bayes Prior | Liuyihan Song, Kang Zhao, Pan Pan, Yu Liu, Yingya Zhang, Yinghui Xu, Rong Jin | Gradient compression has been widely adopted in data-parallel distributed training of deep neural networks to reduce communication overhead. Some literatures have demonstrated that large gradients are more important than small ones because they contain more information, such as Top-k compressor. Other mainstream methods, like random-k compressor and gradient quantization, usually treat all gradients equally. Different from all of them, we regard large and small gradients selection as the exploitation and exploration of gradient information, respectively. And we find taking both of them into consideration is the key to boost the final accuracy. So, we propose a novel gradient compressor: Gradient Sampling with Bayes Prior in this paper. Specifically, we sample important/large gradients based on the global gradient distribution, which is periodically updated across multiple workers. Then we introduce Bayes Prior into distribution model to further explore the gradients. We prove the convergence of our method for smooth non-convex problems in the distributed system. Compared with methods that running after high compression ratio at the expense of accuracy, we pursue no loss of accuracy and the actual acceleration benefit in practice. Experimental comparisons on a variety of computer vision tasks (e.g. image classification and object detection) and backbones (ResNet, MobileNetV2, InceptionV3 and AlexNet) show that our approach outperforms the state-of-the-art techniques in terms of both speed and accuracy, with the limitation of 100* compression ratio. | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Communication_Efficient_SGD_via_Gradient_Sampling_With_Bayes_Prior_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Communication_Efficient_SGD_via_Gradient_Sampling_With_Bayes_Prior_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Communication_Efficient_SGD_via_Gradient_Sampling_With_Bayes_Prior_CVPR_2021_paper.html | CVPR 2021 | null | null |
AdaBins: Depth Estimation Using Adaptive Bins | Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka | We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhat_AdaBins_Depth_Estimation_Using_Adaptive_Bins_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14141 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bhat_AdaBins_Depth_Estimation_Using_Adaptive_Bins_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bhat_AdaBins_Depth_Estimation_Using_Adaptive_Bins_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhat_AdaBins_Depth_Estimation_CVPR_2021_supplemental.zip | null |
VirFace: Enhancing Face Recognition via Unlabeled Shallow Data | Wenyu Li, Tianchu Guo, Pengyu Li, Binghui Chen, Biao Wang, Wangmeng Zuo, Lei Zhang | Recently, exploiting the effect of the unlabeled data for face recognition attracts increasing attention. However, there are still few works considering the situation that the unlabeled data is shallow which widely exists in real-world scenarios. The existing semi-supervised face recognition methods which focus on generating pseudo labels or minimizing softmax classification probabilities of the unlabeled data don't work very well on the unlabeled shallow data. Thus, it is still a challenge on how to effectively utilize the unlabeled shallow face data for improving the performance of face recognition. In this paper, we propose a novel face recognition method, named VirFace, to effectively apply the unlabeled shallow data for face recognition. VirFace consists of VirClass and VirInstance. Specifically, VirClass enlarges the inter-class distance by injecting the unlabeled data as new identities. Furthermore, VirInstance produces virtual instances sampled from the learned distribution of each identity to further enlarge the inter-class distance. To the best of our knowledge, we are the first working on tackling the unlabeled shallow face data. Extensive experiments have been conducted on both the small- and large-scale datasets, e.g. LFW and IJB-C, etc, showing the superiority of the proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_VirFace_Enhancing_Face_Recognition_via_Unlabeled_Shallow_Data_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_VirFace_Enhancing_Face_Recognition_via_Unlabeled_Shallow_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_VirFace_Enhancing_Face_Recognition_via_Unlabeled_Shallow_Data_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_VirFace_Enhancing_Face_CVPR_2021_supplemental.pdf | null |
Pulsar: Efficient Sphere-Based Neural Rendering | Christoph Lassner, Michael Zollhofer | We propose Pulsar, an efficient sphere-based differentiable rendering module that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from image observations. However, gradient-based optimization of neural mesh, voxel, or function representations suffers from multiple challenges, i.e., topological in-consistencies, high memory footprints, or slow rendering speeds. To alleviate these problems, Pulsar employs: 1) asphere-based scene representation, 2) a modular, efficient differentiable projection operation, and 3) (optional) neural shading. Pulsar executes orders of magnitude faster than existing techniques and allows real-time rendering and optimization of representations with millions of spheres. Using spheres for the scene representation, unprecedented speed is obtained while avoiding topology problems. Pulsar is fully differentiable and thus enables a plethora of applications, ranging from 3D reconstruction to neural rendering. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lassner_Pulsar_Efficient_Sphere-Based_Neural_Rendering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.07484 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lassner_Pulsar_Efficient_Sphere-Based_Neural_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lassner_Pulsar_Efficient_Sphere-Based_Neural_Rendering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lassner_Pulsar_Efficient_Sphere-Based_CVPR_2021_supplemental.pdf | null |
Contrastive Learning Based Hybrid Networks for Long-Tailed Image Classification | Peng Wang, Kai Han, Xiu-Shen Wei, Lei Zhang, Lei Wang | Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases. Given the promising performance contrastive learning has shown recently in representation learning, in this work, we explore effective supervised contrastive learning strategies and tailor them to learn better image representations from imbalanced data in order to boost the classification accuracy thereon. Specifically, we propose a novel hybrid network structure being composed of a supervised contrastive loss to learn image representations and a cross-entropy loss to learn classifiers, where the learning is progressively transited from feature learning to the classifier learning to embody the idea that better features make better classifiers. We explore two variants of contrastive loss for feature learning, which vary in the forms but share a common idea of pulling the samples from the same class together in the normalized embedding space and pushing the samples from different classes apart. One of them is the recently proposed supervised contrastive (SC) loss, which is designed on top of the state-of-the-art unsupervised contrastive loss by incorporating positive samples from the same class. The other is a prototypical supervised contrastive (PSC) learning strategy which addresses the intensive memory consumption in standard SC loss and thus shows more promise under limited memory budget. Extensive experiments on three long-tailed classification datasets demonstrate the advantage of the proposed contrastive learning based hybrid networks in long-tailed classification. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Contrastive_Learning_Based_Hybrid_Networks_for_Long-Tailed_Image_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14267 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Contrastive_Learning_Based_Hybrid_Networks_for_Long-Tailed_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Contrastive_Learning_Based_Hybrid_Networks_for_Long-Tailed_Image_Classification_CVPR_2021_paper.html | CVPR 2021 | null | null |
Visualizing Adapted Knowledge in Domain Transfer | Yunzhong Hou, Liang Zheng | A source model trained on source data and a target model learned through unsupervised domain adaptation (UDA) usually encode different knowledge. To understand the adaptation process, we portray their knowledge difference with image translation. Specifically, we feed a translated image and its original version to the two models respectively, formulating two branches. Through updating the translated image, we force similar outputs from the two branches. When such requirements are met, differences between the two images can compensate for and hence represent the knowledge difference between models. To enforce similar outputs from the two branches and depict the adapted knowledge, we propose a source-free image translation method that generates source-style images using only target images and the two models. We visualize the adapted knowledge on several datasets with different UDA methods and find that generated images successfully capture the style difference between the two domains. For application, we show that generated images enable further tuning of the target model without accessing source data. Code available at https://github.com/hou-yz/DA_visualization. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Visualizing_Adapted_Knowledge_in_Domain_Transfer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10602 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Visualizing_Adapted_Knowledge_in_Domain_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Visualizing_Adapted_Knowledge_in_Domain_Transfer_CVPR_2021_paper.html | CVPR 2021 | null | null |
Delving into Data: Effectively Substitute Training for Black-box Attack | Wenxuan Wang, Bangjie Yin, Taiping Yao, Li Zhang, Yanwei Fu, Shouhong Ding, Jilin Li, Feiyue Huang, Xiangyang Xue | Deep models have shown their vulnerability when processing adversarial samples. As for the black-box attack, without access to the architecture and weights of the attacked model, training a substitute model for adversarial attacks has attracted wide attention. Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models. In this paper, we propose a novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process. More specifically, a diverse data generation module is proposed to synthesize large-scale data with wide distribution. And adversarial substitute training strategy is introduced to focus on the data distributed near the decision boundary. The combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack. Extensive experiments demonstrate the efficacy of our method against state-of-the-art competitors under non-target and target attack settings. Detailed visualization and analysis are also provided to help understand the advantage of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Delving_into_Data_Effectively_Substitute_Training_for_Black-box_Attack_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.12378 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Delving_into_Data_Effectively_Substitute_Training_for_Black-box_Attack_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Delving_into_Data_Effectively_Substitute_Training_for_Black-box_Attack_CVPR_2021_paper.html | CVPR 2021 | null | null |
How To Exploit the Transferability of Learned Image Compression to Conventional Codecs | Jan P. Klopp, Keng-Chi Liu, Liang-Gee Chen, Shao-Yi Chien | Lossy image compression is often limited by the simplicity of the chosen loss measure. Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures. Together with learned image compression, these two techniques can be used to great effect when relaxing the commonly employed tight measures of distortion. However, convolutional neural network-based algorithms have a large computational footprint. Ideally, an existing conventional codec should stay in place, ensuring faster adoption and adherence to a balanced computational envelope. As a possible avenue to this goal, we propose and investigate how learned image coding can be used as a surrogate to optimise an image for encoding. A learned filter alters the image to optimise a different performance measure or a particular task. Extending this idea with a generative adversarial network, we show how entire textures are replaced by ones that are less costly to encode but preserve a sense of detail. Our approach can remodel a conventional codec to adjust for the MS-SSIM distortion with over 20% rate improvement without any decoding overhead. On task-aware image compression, we perform favourably against a similar but codec-specific approach. | https://openaccess.thecvf.com/content/CVPR2021/papers/Klopp_How_To_Exploit_the_Transferability_of_Learned_Image_Compression_to_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.01874 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Klopp_How_To_Exploit_the_Transferability_of_Learned_Image_Compression_to_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Klopp_How_To_Exploit_the_Transferability_of_Learned_Image_Compression_to_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Klopp_How_To_Exploit_CVPR_2021_supplemental.pdf | null |
CorrNet3D: Unsupervised End-to-End Learning of Dense Correspondence for 3D Point Clouds | Yiming Zeng, Yue Qian, Zhiyu Zhu, Junhui Hou, Hui Yuan, Ying He | Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D -the first unsupervised and end-to-end deep learning-based framework - to drive the learning of dense correspondence between 3D shapes by means of deformation-like reconstruction to overcome the need for annotated data. Specifically, CorrNet3D consists of a deep feature embedding module and two novel modules called correspondence indicator and symmetric deformer. Feeding a pair of raw point clouds, our model first learns the pointwise features and passes them into the indicator to generate a learnable correspondence matrix used to permute the input pair. The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence. The extensive experiments on both synthetic and real-world datasets of rigid and non-rigid 3D shapes show our CorrNet3D outperforms state-of-the-art methods to a large extent, including those taking meshes as input. CorrNet3D is a flexible framework in that it can be easily adapted to supervised learning if annotated data are available. The source code and pre-trained model will be available at https://github.com/ZENGYIMINGEAMON/CorrNet3D.git. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zeng_CorrNet3D_Unsupervised_End-to-End_Learning_of_Dense_Correspondence_for_3D_Point_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.15638 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_CorrNet3D_Unsupervised_End-to-End_Learning_of_Dense_Correspondence_for_3D_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_CorrNet3D_Unsupervised_End-to-End_Learning_of_Dense_Correspondence_for_3D_Point_CVPR_2021_paper.html | CVPR 2021 | null | null |
Single-View Robot Pose and Joint Angle Estimation via Render & Compare | Yann Labbe, Justin Carpentier, Mathieu Aubry, Josef Sivic | We introduce RoboPose, a method to estimate the joint angles and the 6D camera-to-robot pose of a known articulated robot from a single RGB image. This is an important problem to grant mobile and itinerant autonomous systems the ability to interact with other robots using only visual information in non-instrumented environments, especially in the context of collaborative robotics. It is also challenging because robots have many degrees of freedom and an infinite space of possible configurations that often result in self-occlusions and depth ambiguities when imaged by a single camera. The contributions of this work are three-fold. First, we introduce a new render & compare approach for estimating the 6D pose and joint angles of an articulated robot that can be trained from synthetic data, generalizes to new unseen robot configurations at test time, and can be applied to a variety of robots. Second, we experimentally demonstrate the importance of the robot parametrization for the iterative pose updates and design a parametrization strategy that is independent of the robot structure. Finally, we show experimental results on existing benchmark datasets for four different robots and demonstrate that our method significantly outperforms the state of the art. Code and pre-trained models are available on the project webpage. | https://openaccess.thecvf.com/content/CVPR2021/papers/Labbe_Single-View_Robot_Pose_and_Joint_Angle_Estimation_via_Render__CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09359 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Labbe_Single-View_Robot_Pose_and_Joint_Angle_Estimation_via_Render__CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Labbe_Single-View_Robot_Pose_and_Joint_Angle_Estimation_via_Render__CVPR_2021_paper.html | CVPR 2021 | null | null |
Harmonious Semantic Line Detection via Maximal Weight Clique Selection | Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Chang-Su Kim | A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal process. Third, we construct a complete graph, whose edge weights are computed by H-Net. Finally, we determine a maximal weight clique representing an optimal set of semantic lines. Moreover, to assess the overall harmony of detected lines, we propose a novel metric, called HIoU. Experimental results demonstrate that the proposed algorithm can detect harmonious semantic lines effectively and efficiently. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-MWCS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jin_Harmonious_Semantic_Line_Detection_via_Maximal_Weight_Clique_Selection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jin_Harmonious_Semantic_Line_Detection_via_Maximal_Weight_Clique_Selection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jin_Harmonious_Semantic_Line_Detection_via_Maximal_Weight_Clique_Selection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jin_Harmonious_Semantic_Line_CVPR_2021_supplemental.zip | null |
Learning the Non-Differentiable Optimization for Blind Super-Resolution | Zheng Hui, Jie Li, Xiumei Wang, Xinbo Gao | Previous convolutional neural network (CNN) based blind super-resolution (SR) methods usually adopt an iterative optimization way to approximate the ground-truth (GT) step-by-step. This solution always involves more computational costs to bring about time-consuming inference. At present, most blind SR algorithms are dedicated to obtaining high-fidelity results; their loss function generally employs L1 loss. To further improve the visual quality of SR results, perceptual metric, such as NIQE, is necessary to guide the network optimization. However, due to the non-differentiable property of NIQE, it cannot be as the loss function. Towards these issues, we propose an adaptive modulation network (AMNet) for multiple degradations SR, which is composed of the pivotal adaptive modulation layer (AMLayer). It is an efficient yet lightweight fusion layer between blur kernel and image features. Equipped with the blur kernel predictor, we naturally upgrade the AMNet to the blind SR model. Instead of considering iterative strategy, we make the blur kernel predictor trainable in the whole blind SR model, in which AMNet is well-trained. Also, we fit deep reinforcement learning into the blind SR model (AMNet-RL) to tackle the non-differentiable optimization problem. Specifically, the blur kernel predictor will be the actor to estimate the blur kernel from the input low-resolution (LR) image. The reward is designed by the pre-defined differentiable or non-differentiable metric. Extensive experiments show that our model can outperform state-of-the-art methods in both fidelity and perceptual metrics. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hui_Learning_the_Non-Differentiable_Optimization_for_Blind_Super-Resolution_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hui_Learning_the_Non-Differentiable_Optimization_for_Blind_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hui_Learning_the_Non-Differentiable_Optimization_for_Blind_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hui_Learning_the_Non-Differentiable_CVPR_2021_supplemental.pdf | null |
Progressive Temporal Feature Alignment Network for Video Inpainting | Xueyan Zou, Linjie Yang, Ding Liu, Yong Jae Lee | Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content. Current methods achieve this goal through attention, flow-based warping, or 3D temporal convolution. However, flow-based warping can create artifacts when optical flow is not accurate, while temporal convolution may suffer from spatial misalignment. We propose `Progressive Temporal Feature Alignment Network', which progressively enriches features extracted from the current frame with the feature warped from neighbouring frames using optical flow. Our approach corrects the spatial misalignment in the temporal feature propagation stage, greatly improving visual quality and temporal consistency of the inpainted videos. Using the proposed architecture, we achieve state-of-the-art performance on the DAVIS and FVI datasets compared to existing deep learning approaches. Code is available at https://github.com/MaureenZOU/TSAM. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zou_Progressive_Temporal_Feature_Alignment_Network_for_Video_Inpainting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03507 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_Progressive_Temporal_Feature_Alignment_Network_for_Video_Inpainting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_Progressive_Temporal_Feature_Alignment_Network_for_Video_Inpainting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zou_Progressive_Temporal_Feature_CVPR_2021_supplemental.pdf | null |
Bottleneck Transformers for Visual Recognition | Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani | We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck blocks of a ResNet and no other changes, our approach improves upon the baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency. Through the design of BoTNet, we also point out how ResNet bottleneck blocks with self-attention can be viewed as Transformer blocks. Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework; surpassing the previous best published single model and single scale results of ResNeSt evaluated on the COCO validation set. Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 1.64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision. | https://openaccess.thecvf.com/content/CVPR2021/papers/Srinivas_Bottleneck_Transformers_for_Visual_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.11605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Srinivas_Bottleneck_Transformers_for_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Srinivas_Bottleneck_Transformers_for_Visual_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Srinivas_Bottleneck_Transformers_for_CVPR_2021_supplemental.pdf | null |
Calibrated RGB-D Salient Object Detection | Wei Ji, Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao, Qi Bi, Kai Ma, Yefeng Zheng, Huchuan Lu, Li Cheng | Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware SOD. Meanwhile, this emerging line of research has been considerably hindered by the noise and ambiguity that prevail in raw depth images. To address the aforementioned issues, we propose a Depth Calibration and Fusion (DCF) framework that contains two novel components: 1) a learning strategy to calibrate the latent bias in the original depth maps towards boosting the SOD performance; 2) a simple yet effective cross reference module to fuse features from both RGB and depth modalities. Extensive empirical experiments demonstrate that the proposed approach achieves superior performance against 27 state-of-the-art methods. Moreover, the proposed depth calibration strategy as a preprocessing step, can be further applied to existing cutting-edge RGB-D SOD models and noticeable improvements are achieved. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling | Ze Yang, Shenlong Wang, Sivabalan Manivasagam, Zeng Huang, Wei-Chiu Ma, Xinchen Yan, Ersin Yumer, Raquel Urtasun | Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation. As there are exponentially many variations of humans with different shape, pose and clothing, it is critical to develop methods that can automatically reconstruct and animate humans at scale from real world data. Towards this goal, we represent the pedestrian's shape, pose and skinning weights as neural implicit functions that are directly learned from data. This representation enables us to handle a wide variety of different pedestrian shapes and poses without explicitly fitting a human parametric body model, allowing us to handle a wider range of human geometries and topologies. We demonstrate the effectiveness of our approach on various datasets and show that our reconstructions outperform existing state-of-the-art methods. Furthermore, our re-animation experiments show that we can generate 3D human animations at scale from a single RGB image (and/or an optional LiDAR sweep) as input. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_S3_Neural_Shape_Skeleton_and_Skinning_Fields_for_3D_Human_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.06571 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_S3_Neural_Shape_Skeleton_and_Skinning_Fields_for_3D_Human_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_S3_Neural_Shape_Skeleton_and_Skinning_Fields_for_3D_Human_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_S3_Neural_Shape_CVPR_2021_supplemental.zip | null |
OSTeC: One-Shot Texture Completion | Baris Gecer, Jiankang Deng, Stefanos Zafeiriou | The last few years have witnessed the great success of non-linear generative models in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Adversarial Networks (GANs). Yet the collection of such a large scale high-resolution 3D texture dataset is still very costly and difficult to maintain age/ethnicity balance. Moreover, regression-based approaches suffer from generalization to the in-the-wild conditions and are unable to fine-tune to a target-image. In this work, we propose an unsupervised approach for one-shot 3D facial texture completion that does not require large-scale texture datasets, but rather harnesses the knowledge stored in 2D face generators. The proposed approach rotates an input image in 3D and fill-in the unseen regions by reconstructing the rotated image in a 2D face generator, based on the visible parts. Finally, we stitch the most visible textures at different angles in the UV image-plane. Further, we frontalize the target image by projecting the completed texture into the generator. The qualitative and quantitative experiments demonstrate that the completed UV textures and frontalized images are of high quality, resembles the original identity, can be used to train a texture GAN model for 3DMM fitting and improve pose-invariant face recognition. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.15370 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gecer_OSTeC_One-Shot_Texture_CVPR_2021_supplemental.pdf | null |
Learning To Count Everything | Viresh Ranjan, Udbhav Sharma, Thu Nguyen, Minh Hoai | Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://github.com/cvlab-stonybrook/LearningToCountEverything. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ranjan_Learning_To_Count_Everything_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08391 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ranjan_Learning_To_Count_Everything_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ranjan_Learning_To_Count_Everything_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ranjan_Learning_To_Count_CVPR_2021_supplemental.pdf | null |
Robust Representation Learning With Feedback for Single Image Deraining | Chenghao Chen, Hao Li | A deraining network can be interpreted as a conditional generator that aims at removing rain streaks from image. Most existing image deraining methods ignore model errors caused by uncertainty that reduces embedding quality. Unlike existing image deraining methods that embed low-quality features into the model directly, we replace low-quality features by latent high-quality features. The spirit of closed-loop feedback in the automatic control field is borrowed to obtain latent high-quality features. A new method for error detection and feature compensation is proposed to address model errors. Extensive experiments on benchmark datasets as well as specific real datasets demonstrate that the proposed method outperforms recent state-of-the-art methods. Code is available at: https://github.com/LI-Hao-SJTU/DerainRLNet | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.12463 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.html | CVPR 2021 | null | null |
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction | Feng Liu, Luan Tran, Xiaoming Liu | Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CAD-generated synthetic data, which have difficulty in inference from real images, or generate 2.5D depth image via intrinsic decomposition, which is limited compared to the full 3D reconstruction. One fundamental challenge lies in how to leverage numerous real 2D images without any 3D ground truth. To address this issue, we take an alternative approach with semi-supervised learning. That is, for a 2D image of a generic object, we decompose it into latent representations of category, shape and albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedo respectively, and fuse these components to render an image well approximating the input image. Using a category-adaptive 3D joint occupancy field (JOF), we show that the complete shape and albedo modeling enables us to leverage real 2D images in both modeling and model fitting. The effectiveness of our approach is demonstrated through superior 3D reconstruction from a single image, being either synthetic or real, and shape segmentation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Fully_Understanding_Generic_Objects_Modeling_Segmentation_and_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00858 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Fully_Understanding_Generic_Objects_Modeling_Segmentation_and_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Fully_Understanding_Generic_Objects_Modeling_Segmentation_and_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Fully_Understanding_Generic_CVPR_2021_supplemental.zip | null |
SSN: Soft Shadow Network for Image Compositing | Yichen Sheng, Jianming Zhang, Bedrich Benes | We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to image types such as painting and vector art. An environment light map is used to control the shadow's characteristics, such as angle and softness. SSN employs an Ambient Occlusion Prediction module to predict an intermediate ambient occlusion map, which can be further refined by the user to provides geometric cues to modulate the shadow generation. To train our model, we design an efficient pipeline to produce diverse soft shadow training data using 3D object models. In addition, we propose an inverse shadow map representation to improve model training. We demonstrate that our model produces realistic soft shadows in real-time. Our user studies show that the generated shadows are often indistinguishable from shadows calculated by a physics-based renderer and users can easily use SSN through an interactive application to generate specific shadow effects in minutes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sheng_SSN_Soft_Shadow_Network_for_Image_Compositing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.08211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sheng_SSN_Soft_Shadow_Network_for_Image_Compositing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sheng_SSN_Soft_Shadow_Network_for_Image_Compositing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sheng_SSN_Soft_Shadow_CVPR_2021_supplemental.zip | null |
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection | Jia-Chang Feng, Fa-Ting Hong, Wei-Shi Zheng | Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insufficient video representations. In this work, we develop a multiple instance self-training framework (MIST) to efficiently refine task-specific discriminative representations with only video-level annotations. In particular, MIST is composed of 1) a multiple instance pseudo label generator, which adapts a sparse continuous sampling strategy to produce more reliable clip-level pseudo labels, and 2) a self-guided attention boosted feature encoder that aims to automatically focus on anomalous regions in frames while extracting task-specific representations. Moreover, we adopt a self-training scheme to optimize both components and finally obtain a task-specific feature encoder. Extensive experiments on two public datasets demonstrate the efficacy of our method, and our method performs comparably or even better with existing supervised and weakly supervised methods, specifically obtaining a frame-level AUC 94.83% on ShanghaiTech. | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_MIST_Multiple_Instance_Self-Training_Framework_for_Video_Anomaly_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01633 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_MIST_Multiple_Instance_Self-Training_Framework_for_Video_Anomaly_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_MIST_Multiple_Instance_Self-Training_Framework_for_Video_Anomaly_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Feng_MIST_Multiple_Instance_CVPR_2021_supplemental.pdf | null |
VinVL: Revisiting Visual Representations in Vision-Language Models | Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao | This paper presents a detailed study of improving vision features and develops an improved object detection model for vision language (VL) tasks. Compared to the most widely used bottom-up and top-down model [2], the new model is bigger, pre-trained on much larger training corpora that combine multiple public annotated object detection datasets, and thus can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses solely on improving the vision-language fusion model and leaves the object detection model improvement untouched, we present an empirical study to show that vision features matter significantly in VL models. In our experiments we feed the vision features generated by the new object detection model into a pre-trained transformer-based VL fusion model Oscar+, and fine-tune Oscar+ on a wide range of downstream VL tasks. Our results show that the new vision features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_VinVL_Revisiting_Visual_Representations_in_Vision-Language_Models_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.00529 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_VinVL_Revisiting_Visual_Representations_in_Vision-Language_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_VinVL_Revisiting_Visual_Representations_in_Vision-Language_Models_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_VinVL_Revisiting_Visual_CVPR_2021_supplemental.pdf | null |
Bottom-Up Human Pose Estimation via Disentangled Keypoint Regression | Zigang Geng, Ke Sun, Bin Xiao, Zhaoxiang Zhang, Jingdong Wang | In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions. We present a simple yet effective approach, named disentangled keypoint regression (DEKR). We adopt adaptive convolutions through pixel-wise spatial transformer to activate the pixels in the keypoint regions and accordingly learn representations from them. We use a multi-branch structure for separate regression: each branch learns a representation with dedicated adaptive convolutions and regresses one keypoint. The resulting disentangled representations are able to attend to the keypoint regions, respectively, and thus the keypoint regression is spatially more accurate. We empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods and achieves superior bottom-up pose estimation results on two benchmark datasets, COCO and CrowdPose. The code and models are available at https://github.com/HRNet/DEKR. | https://openaccess.thecvf.com/content/CVPR2021/papers/Geng_Bottom-Up_Human_Pose_Estimation_via_Disentangled_Keypoint_Regression_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Geng_Bottom-Up_Human_Pose_Estimation_via_Disentangled_Keypoint_Regression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Geng_Bottom-Up_Human_Pose_Estimation_via_Disentangled_Keypoint_Regression_CVPR_2021_paper.html | CVPR 2021 | null | null |
CoMoGAN: Continuous Model-Guided Image-to-Image Translation | Fabio Pizzati, Pietro Cerri, Raoul de Charette | CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normalization layer and residual mechanism, which together disentangle image content from position on target manifold. We rely on naive physics-inspired models to guide the training while allowing private model/translations features. CoMoGAN can be used with any GAN backbone and allows new types of image translation, such as cyclic image translation like timelapse generation, or detached linear translation. On all datasets, it outperforms the literature. Our code is available in this page: https://github.com/cv-rits/CoMoGAN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pizzati_CoMoGAN_Continuous_Model-Guided_Image-to-Image_Translation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06879 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pizzati_CoMoGAN_Continuous_Model-Guided_Image-to-Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pizzati_CoMoGAN_Continuous_Model-Guided_Image-to-Image_Translation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pizzati_CoMoGAN_Continuous_Model-Guided_CVPR_2021_supplemental.zip | null |
Self-Supervised Video Hashing via Bidirectional Transformers | Shuyan Li, Xiu Li, Jiwen Lu, Jie Zhou | Most existing unsupervised video hashing methods are built on unidirectional models with less reliable training objectives, which underuse the correlations among frames and the similarity structure between videos. To enable efficient scalable video retrieval, we propose a self-supervised video Hashing method based on Bidirectional Transformers (BTH). Based on the encoder-decoder structure of transformers, we design a visual cloze task to fully exploit the bidirectional correlations between frames. To unveil the similarity structure between unlabeled video data, we further develop a similarity reconstruction task by establishing reliable and effective similarity connections in the video space. Furthermore, we develop a cluster assignment task to exploit the structural statistics of the whole dataset such that more discriminative binary codes can be learned. Extensive experiments implemented on three public benchmark datasets, FCVID, ActivityNet and YFCC, demonstrate the superiority of our proposed approach. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Self-Supervised_Video_Hashing_via_Bidirectional_Transformers_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Self-Supervised_Video_Hashing_via_Bidirectional_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Self-Supervised_Video_Hashing_via_Bidirectional_Transformers_CVPR_2021_paper.html | CVPR 2021 | null | null |
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation | Chen Li, Gim Hee Lee | Animal pose estimation is an important field that has received increasing attention in the recent years. The main challenge for this task is the lack of labeled data. Existing works circumvent this problem with pseudo labels generated from data of other easily accessible domains such as synthetic data. However, these pseudo labels are noisy even with consistency check or confidence-based filtering due to the domain shift in the data. To solve this problem, we design a multi-scale domain adaptation module (MDAM) to reduce the domain gap between the synthetic and real data. We further introduce an online coarse-to-fine pseudo label updating strategy. Specifically, we propose a self-distillation module in an inner coarse-update loop and a mean-teacher in an outer fine-update loop to generate new pseudo labels that gradually replace the old ones. Consequently, our model is able to learn from the old pseudo labels at the early stage, and gradually switch to the new pseudo labels to prevent overfitting in the later stage. We evaluate our approach on the TigDog and VisDA 2019 datasets, where we outperform existing approaches by a large margin. We also demonstrate the generalization ability of our model by testing extensively on both unseen domains and unseen animal categories. Our code is available at the project website. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_From_Synthetic_to_Real_Unsupervised_Domain_Adaptation_for_Animal_Pose_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14843 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_From_Synthetic_to_Real_Unsupervised_Domain_Adaptation_for_Animal_Pose_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_From_Synthetic_to_Real_Unsupervised_Domain_Adaptation_for_Animal_Pose_CVPR_2021_paper.html | CVPR 2021 | null | null |
Safe Local Motion Planning With Self-Supervised Freespace Forecasting | Peiyun Hu, Aaron Huang, John Dolan, David Held, Deva Ramanan | Safe local motion planning for autonomous driving in dynamic environments requires forecasting how the scene evolves. Practical autonomy stacks adopt a semantic object-centric representation of a dynamic scene and build object detection, tracking, and prediction modules to solve forecasting. However, training these modules comes at an enormous human cost of manually annotated objects across frames. In this work, we explore future freespace as an alternative representation to support motion planning. Our key intuition is that it is important to avoid straying into occupied space regardless of what is occupying it. Importantly, computing ground-truth future freespace is annotation-free. First, we explore freespace forecasting as a self-supervised learning task. We then demonstrate how to use forecasted freespace to identify collision-prone plans from off-the-shelf motion planners. Finally, we propose future freespace as an additional source of annotation-free supervision. We demonstrate how to integrate such supervision into the learning-based planners. Experimental results on nuScenes and CARLA suggest both approaches lead to a significant reduction in collision rates. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Safe_Local_Motion_Planning_With_Self-Supervised_Freespace_Forecasting_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Safe_Local_Motion_Planning_With_Self-Supervised_Freespace_Forecasting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Safe_Local_Motion_Planning_With_Self-Supervised_Freespace_Forecasting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Safe_Local_Motion_CVPR_2021_supplemental.zip | null |
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CVPR 2021 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2021 Open Access website (https://openaccess.thecvf.com/CVPR2021) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2021). For researchers who are interested in doing analysis of CVPR 2021 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2021 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification",
"authors": "Haowei Zhu, Wenjing Ke, Dong Li, Ji Liu, Lu Tian, Yi Shan",
"abstract": "Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore how to extend self-attention modules to better learn subtle feature embeddings for recognizing fine-grained objects, e.g., different bird species or person identities. To this end, we propose a dual cross-attention learning (DCAL) algorithm to coordinate with self-attention learning. First, we propose global-local cross-attention (GLCA) to enhance the interactions between global images and local high-response regions, which can help reinforce the spatial-wise discriminative clues for recognition. Second, we propose pair-wise cross-attention (PWCA) to establish the interactions between image pairs. PWCA can regularize the attention learning of an image by treating another image as distractor and will be removed during inference. We observe that DCAL can reduce misleading attentions and diffuse the attention response to discover more complementary parts for recognition. We conduct extensive evaluations on fine-grained visual categorization and object re-identification. Experiments demonstrate that DCAL performs on par with state-of-the-art methods and consistently improves multiple self-attention baselines, e.g., surpassing DeiT-Tiny and ViT-Base by 2.8% and 2.4% mAP on MSMT17, respectively.",
"pdf": "https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.pdf",
"supp": "https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Dual_Cross-Attention_Learning_CVPR_2022_supplemental.pdf",
"arXiv": "http://arxiv.org/abs/2205.02151",
"bibtex": "https://openaccess.thecvf.com",
"url": "https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html",
"detail_url": "https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html",
"tags": "CVPR 2022"
}
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