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High-Quality Stereo Image Restoration From Double Refraction | Hakyeong Kim, Andreas Meuleman, Daniel S. Jeon, Min H. Kim | Single-shot monocular birefractive stereo methods have been used for estimating sparse depth from double refraction over edges. They also obtain an ordinary-ray (o-ray) image concurrently or subsequently through additional post-processing of depth densification and deconvolution. However, when an extraordinary-ray (e-ray) image is restored to acquire stereo images, the existing methods suffer from very severe restoration artifacts in stereo images due to a low signal-to-noise ratio of input e-ray image or depth/deconvolution errors. In this work, we present a novel stereo image restoration network that can restore stereo images directly from a double-refraction image. First, we built a physically faithful birefractive stereo imaging dataset by simulating the double refraction phenomenon with existing RGB-D datasets. Second, we formulated a joint stereo restoration problem that accounts for not only geometric relation between o-/e-ray images but also joint optimization of restoring both stereo images. We trained our model with our birefractive image dataset in an end-to-end manner. Our model restores high-quality stereo images directly from double refraction in real-time, enabling high-quality stereo video using a monocular camera. Our method also allows us to estimate dense depth maps from stereo images using a conventional stereo method. We evaluate the performance of our method experimentally and synthetically with the ground truth. Results validate that our stereo image restoration network outperforms the existing methods with high accuracy. We demonstrate several image-editing applications using our high-quality stereo images and dense depth maps. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_High-Quality_Stereo_Image_Restoration_From_Double_Refraction_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_High-Quality_Stereo_Image_Restoration_From_Double_Refraction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_High-Quality_Stereo_Image_Restoration_From_Double_Refraction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_High-Quality_Stereo_Image_CVPR_2021_supplemental.zip | null |
Track, Check, Repeat: An EM Approach to Unsupervised Tracking | Adam W. Harley, Yiming Zuo, Jing Wen, Ayush Mangal, Shubhankar Potdar, Ritwick Chaudhry, Katerina Fragkiadaki | We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos. The method begins with classic handcrafted techniques for segmenting objects using motion cues: we estimate optical flow and camera motion, and conservatively segment regions that appear to be moving independently of the background. Treating these initial segments as pseudo-labels, we learn an ensemble of appearance-based 2D and 3D detectors, under heavy data augmentation. We use this ensemble to detect new instances of the "moving" type, even if they are not moving, and add these as new pseudo-labels. Our method is an expectation-maximization algorithm, where in the expectation step we fire all modules and look for agreement among them, and in the maximization step we re-train the modules to improve this agreement. The constraint of ensemble agreement helps combat contamination of the generated pseudo-labels (during the E step), and data augmentation helps the modules generalize to yet-unlabelled data (during the M step). We compare against existing unsupervised object discovery and tracking methods, using challenging videos from CATER and KITTI, and show strong improvements over the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Harley_Track_Check_Repeat_An_EM_Approach_to_Unsupervised_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Harley_Track_Check_Repeat_An_EM_Approach_to_Unsupervised_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Harley_Track_Check_Repeat_An_EM_Approach_to_Unsupervised_Tracking_CVPR_2021_paper.html | CVPR 2021 | null | null |
LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity | Cheng-Fu Yang, Wan-Cyuan Fan, Fu-En Yang, Yu-Chiang Frank Wang | When translating text inputs into layouts or images, existing works typically require explicit descriptions of each object in a scene, including their spatial information or the associated relationships. To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout generation, we propose a LayoutTransformer Network (LT-Net) in this paper. Given a scene-graph input, our LT-Net uniquely encodes the semantic features for exploiting their co-occurrences and implicit relationships. This allows one to manipulate conceptually diverse yet plausible layout outputs. Moreover, the decoder of our LT-Net translates the encoded contextual features into bounding boxes with self-supervised relation consistency preserved. By fitting their distributions to Gaussian mixture models, spatially-diverse layouts can be additionally produced by LT-Net. We conduct extensive experiments on the datasets of MS-COCO and Visual Genome, and confirm the effectiveness and plausibility of our LT-Net over recent layout generation models. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_LayoutTransformer_Scene_Layout_Generation_With_Conceptual_and_Spatial_Diversity_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_LayoutTransformer_Scene_Layout_Generation_With_Conceptual_and_Spatial_Diversity_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_LayoutTransformer_Scene_Layout_Generation_With_Conceptual_and_Spatial_Diversity_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_LayoutTransformer_Scene_Layout_CVPR_2021_supplemental.pdf | null |
Practical Wide-Angle Portraits Correction With Deep Structured Models | Jing Tan, Shan Zhao, Pengfei Xiong, Jiangyu Liu, Haoqiang Fan, Shuaicheng Liu | Wide-angle portraits often enjoy expanded views. However, they contain perspective distortions, especially noticeable when capturing group portrait photos, where the background is skewed and faces are stretched. This paper introduces the first deep learning based approach to remove such artifacts from freely-shot photos. Specifically, given a wide-angle portrait as input, we build a cascaded network consisting of a LineNet, a ShapeNet, and a transition module (TM), which corrects perspective distortions on the background, adapts to the stereographic projection on facial regions, and achieves smooth transitions between these two projections, accordingly. To train our network, we build the first perspective portrait dataset with a large diversity in identities, scenes and camera modules. For the quantitative evaluation, we introduce two novel metrics, line consistency and face congruence. Compared to the previous state-of-the-art approach, our method does not require camera distortion parameters. We demonstrate that our approach significantly outperforms the previous state-of-the-art approach both qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_Practical_Wide-Angle_Portraits_Correction_With_Deep_Structured_Models_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.12464 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Practical_Wide-Angle_Portraits_Correction_With_Deep_Structured_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Practical_Wide-Angle_Portraits_Correction_With_Deep_Structured_Models_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_Practical_Wide-Angle_Portraits_CVPR_2021_supplemental.pdf | null |
CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild | Bastian Wandt, Marco Rudolph, Petrissa Zell, Helge Rhodin, Bodo Rosenhahn | Human pose estimation from single images is a challenging problem in computer vision that requires large amounts of labeled training data to be solved accurately. Unfortunately, for many human activities (e.g. outdoor sports) such training data does not exist and is hard or even impossible to acquire with traditional motion capture systems. We propose a self-supervised approach that learns a single image 3D pose estimator from unlabeled multi-view data. To this end, we exploit multi-view consistency constraints to disentangle the observed 2D pose into the underlying 3D pose and camera rotation. In contrast to most existing methods, we do not require calibrated cameras and can therefore learn from moving cameras. Nevertheless, in the case of a static camera setup, we present an optional extension to include constant relative camera rotations over multiple views into our framework. Key to the success are new, unbiased reconstruction objectives that mix information across views and training samples. The proposed approach is evaluated on two benchmark datasets (Human3.6M and MPII-INF-3DHP) and on the in-the-wild SkiPose dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wandt_CanonPose_Self-Supervised_Monocular_3D_Human_Pose_Estimation_in_the_Wild_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14679 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wandt_CanonPose_Self-Supervised_Monocular_3D_Human_Pose_Estimation_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wandt_CanonPose_Self-Supervised_Monocular_3D_Human_Pose_Estimation_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | null | null |
Pushing It Out of the Way: Interactive Visual Navigation | Kuo-Hao Zeng, Luca Weihs, Ali Farhadi, Roozbeh Mottaghi | We have observed significant progress in visual navigation for embodied agents. A common assumption in studying visual navigation is that the environments are static; this is a limiting assumption. Intelligent navigation may involve interacting with the environment beyond just moving forward/backward and turning left/right. Sometimes, the best way to navigate is to push something out of the way. In this paper, we study the problem of interactive navigation where agents learn to change the environment to navigate more efficiently to their goals. To this end, we introduce the Neural Interaction Engine (NIE) to explicitly predict the change in the environment caused by the agent's actions. By modeling the changes while planning, we find that agents exhibit significant improvements in their navigational capabilities. More specifically, we consider two downstream tasks in the physics-enabled, visually rich, AI2-THOR environment: (1) reaching a target while the path to the target is blocked (2) moving an object to a target location by pushing it. For both tasks, agents equipped with an NIE significantly outperform agents without the understanding of the effect of the actions indicating the benefits of our approach. The code and dataset are available at github.com/KuoHaoZeng/Interactive_Visual_Navigation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zeng_Pushing_It_Out_of_the_Way_Interactive_Visual_Navigation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14040 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_Pushing_It_Out_of_the_Way_Interactive_Visual_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_Pushing_It_Out_of_the_Way_Interactive_Visual_Navigation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zeng_Pushing_It_Out_CVPR_2021_supplemental.zip | null |
Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation | Liwei Wang, Jing Huang, Yin Li, Kun Xu, Zhengyuan Yang, Dong Yu | Weakly supervised phrase grounding aims at learning region-phrase correspondences using only image-sentence pairs. A major challenge thus lies in the missing links between image regions and sentence phrases during training. To address this challenge, we leverage a generic object detector at training time, and propose a contrastive learning framework that accounts for both region-phrase and image-sentence matching. Our core innovation is the learning of a region-phrase score function, based on which an image-sentence score function is further constructed. Importantly, our region-phrase score function is learned by distilling from soft matching scores between the detected object names and candidate phrases within an image-sentence pair, while the image-sentence score function is supervised by ground-truth image-sentence pairs. The design of such score functions removes the need of object detection at test time, thereby significantly reducing the inference cost. Without bells and whistles, our approach achieves state-of-the-art results on visual phrase grounding, surpassing previous methods that require expensive object detectors at test time. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Improving_Weakly_Supervised_Visual_Grounding_by_Contrastive_Knowledge_Distillation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.01951 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Improving_Weakly_Supervised_Visual_Grounding_by_Contrastive_Knowledge_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Improving_Weakly_Supervised_Visual_Grounding_by_Contrastive_Knowledge_Distillation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Improving_Weakly_Supervised_CVPR_2021_supplemental.pdf | null |
EvDistill: Asynchronous Events To End-Task Learning via Bidirectional Reconstruction-Guided Cross-Modal Knowledge Distillation | Lin Wang, Yujeong Chae, Sung-Hoon Yoon, Tae-Kyun Kim, Kuk-Jin Yoon | Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur, showing advantages over the conventional cameras. A hurdle of training event-based models is the lack of large qualitative labeled data. Prior works learning end-tasks mostly rely on labeled or pseudo-labeled datasets obtained from the active pixel sensor (APS) frames; however, such datasets' quality is far from rivaling those based on the canonical images. In this paper, we propose a novel approach, called EvDistill, to learn a student network on the unlabeled and unpaired event data (target modality) via knowledge distillation (KD) from a teacher network trained with large labeled image data (source modality). To enable KD across the unpaired modalities, we first propose a bidirectional modality reconstruction (BMR) module to bridge both modalities and simultaneously exploit them to distill knowledge via the crafted pairs, causing no extra computation in the test time. The BMR is improved by the end-task and KD losses in an end-to-end manner. Second, we leverage the structural similarities of both modalities and adapt the knowledge by matching their distributions. Moreover, as most prior feature KD methods are uni-modality and less applicable to our problem, we propose an affinity graph KD and other losses to boost the distillation. Our extensive experiments on semantic segmentation and object recognition demonstrate that EvDistill achieves significantly better results than the prior works and KD with only events and APS frames. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_EvDistill_Asynchronous_Events_To_End-Task_Learning_via_Bidirectional_Reconstruction-Guided_Cross-Modal_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_EvDistill_Asynchronous_Events_To_End-Task_Learning_via_Bidirectional_Reconstruction-Guided_Cross-Modal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_EvDistill_Asynchronous_Events_To_End-Task_Learning_via_Bidirectional_Reconstruction-Guided_Cross-Modal_CVPR_2021_paper.html | CVPR 2021 | null | null |
LoFTR: Detector-Free Local Feature Matching With Transformers | Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, Xiaowei Zhou | We present a novel method for local image feature matching. Instead of performing image feature detection, description, and matching sequentially, we propose to first establish pixel-wise dense matches at a coarse level and later refine the good matches at a fine level. In contrast to dense methods that use a cost volume to search correspondences, we use self and cross attention layers in Transformer to obtain feature descriptors that are conditioned on both images. The global receptive field provided by Transformer enables our method to produce dense matches in low-texture areas, where feature detectors usually struggle to produce repeatable interest points. The experiments on indoor and outdoor datasets show that LoFTR outperforms state-of-the-art methods by a large margin. LoFTR also ranks first on two public benchmarks of visual localization among the published methods. Code is available at our project page: https://zju3dv.github.io/loftr/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_LoFTR_Detector-Free_Local_Feature_Matching_With_Transformers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00680 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_LoFTR_Detector-Free_Local_Feature_Matching_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_LoFTR_Detector-Free_Local_Feature_Matching_With_Transformers_CVPR_2021_paper.html | CVPR 2021 | null | null |
Combinatorial Learning of Graph Edit Distance via Dynamic Embedding | Runzhong Wang, Tianqi Zhang, Tianshu Yu, Junchi Yan, Xiaokang Yang | Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Traditional A* algorithm suffers scalability issues due to its exhaustive nature, whose search heuristics heavily rely on human prior knowledge. This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver. Inspired by dynamic programming, node-level embedding is designated in a dynamic reuse fashion and suboptimal branches are encouraged to be pruned. To this end, our method can be readily integrated into A* procedure in a dynamic fashion, as well as significantly reduce the computational burden with a learned heuristic. Experimental results on different graph datasets show that our approach can remarkably ease the search process of A* without sacrificing much accuracy. To our best knowledge, this work is also the first deep learning-based GED method for recovering the edit path. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Combinatorial_Learning_of_Graph_Edit_Distance_via_Dynamic_Embedding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.15039 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Combinatorial_Learning_of_Graph_Edit_Distance_via_Dynamic_Embedding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Combinatorial_Learning_of_Graph_Edit_Distance_via_Dynamic_Embedding_CVPR_2021_paper.html | CVPR 2021 | null | null |
Radar-Camera Pixel Depth Association for Depth Completion | Yunfei Long, Daniel Morris, Xiaoming Liu, Marcos Castro, Punarjay Chakravarty, Praveen Narayanan | While radar and video data can be readily fused at the detection level, fusing them at the pixel level is potentially more beneficial. This is also more challenging in part due to the sparsity of radar, but also because automotive radar beams are much wider than a typical pixel combined with a large baseline between camera and radar, which results in poor association between radar pixels and color pixel. A consequence is that depth completion methods designed for LiDAR and video fare poorly for radar and video. Here we propose a radar-to-pixel association stage which learns a mapping from radar returns to pixels. This mapping also serves to densify radar returns. Using this as a first stage, followed by a more traditional depth completion method, we are able to achieve image-guided depth completion with radar and video. We demonstrate performance superior to camera and radar alone on the nuScenes dataset. Our source code is available at https://github.com/longyunf/rc-pda. | https://openaccess.thecvf.com/content/CVPR2021/papers/Long_Radar-Camera_Pixel_Depth_Association_for_Depth_Completion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.02778 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Long_Radar-Camera_Pixel_Depth_Association_for_Depth_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Long_Radar-Camera_Pixel_Depth_Association_for_Depth_Completion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Long_Radar-Camera_Pixel_Depth_CVPR_2021_supplemental.zip | null |
Improved Image Matting via Real-Time User Clicks and Uncertainty Estimation | Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao, Hanqing Zhao, Weiming Zhang, Nenghai Yu | Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous, thus restricting the application of these methods. Recently, some trimap-free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, without the trimap guidance in some cases, the target network is ambiguous about which is the foreground target. In fact, choosing the foreground is a subjective procedure and depends on the user's intention. To this end, this paper proposes an improved deep image matting framework which is trimap-free and only needs several user click interactions to eliminate the ambiguity. Moreover, we introduce a new uncertainty estimation module that can predict which parts need polishing and a following local refinement module. Based on the computation budget, users can choose how many local parts to improve with the uncertainty guidance. Quantitative and qualitative results show that our method performs better than existing trimap-free methods and comparably to state-of-the-art trimap-based methods with minimal user effort. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_Improved_Image_Matting_via_Real-Time_User_Clicks_and_Uncertainty_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.08323 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_Improved_Image_Matting_via_Real-Time_User_Clicks_and_Uncertainty_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_Improved_Image_Matting_via_Real-Time_User_Clicks_and_Uncertainty_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_Improved_Image_Matting_CVPR_2021_supplemental.pdf | null |
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs | Lile Cai, Xun Xu, Jun Hao Liew, Chuan Sheng Foo | State-of-the-art methods for semantic segmentation are based on deep neural networks that are known to be data-hungry. Region-based active learning has shown to be a promising method for reducing data annotation costs. A key design choice for region-based AL is whether to use regularly-shaped regions (e.g., rectangles) or irregularly-shaped region (e.g., superpixels). In this work, we address this question under realistic, click-based measurement of annotation costs. In particular, we revisit the use of superpixels and demonstrate that the inappropriate choice of cost measure (e.g., the percentage of labeled pixels), may cause the effectiveness of the superpixel-based approach to be under-estimated. We benchmark the superpixel-based approach against the traditional "rectangle+polygon"-based approach with annotation cost measured in clicks, and show that the former outperforms on both Cityscapes and PASCAL VOC. We further propose a class-balanced acquisition function to boost the performance of the superpixel-based approach and demonstrate its effectiveness on the evaluation datasets. Our results strongly argue for the use of superpixel-based AL for semantic segmentation and highlight the importance of using realistic annotation costs in evaluating such methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Revisiting_Superpixels_for_Active_Learning_in_Semantic_Segmentation_With_Realistic_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Revisiting_Superpixels_for_Active_Learning_in_Semantic_Segmentation_With_Realistic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Revisiting_Superpixels_for_Active_Learning_in_Semantic_Segmentation_With_Realistic_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cai_Revisiting_Superpixels_for_CVPR_2021_supplemental.pdf | null |
IMODAL: Creating Learnable User-Defined Deformation Models | Leander Lacroix, Benjamin Charlier, Alain Trouve, Barbara Gris | A natural way to model the evolution of an object (growth of a leaf for instance) is to estimate a plausible deforming path between two observations. This interpolation process can generate deceiving results when the set of considered deformations is not relevant to the observed data. To overcome this issue, the framework of deformation modules allows to incorporate in the model structured deformation patterns coming from prior knowledge on the data. The goal of this article is twofold. First defining new deformation modules incorporating structures coming from the elastic properties of the objects. Second, presenting the IMODAL library allowing to perform registration through structured deformations. This library is modular: adapted priors can be easily defined by the user, several priors can be combined into a global one and various types of data can be considered such as curves, meshes or images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lacroix_IMODAL_Creating_Learnable_User-Defined_Deformation_Models_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lacroix_IMODAL_Creating_Learnable_User-Defined_Deformation_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lacroix_IMODAL_Creating_Learnable_User-Defined_Deformation_Models_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lacroix_IMODAL_Creating_Learnable_CVPR_2021_supplemental.pdf | null |
Fast End-to-End Learning on Protein Surfaces | Freyr Sverrisson, Jean Feydy, Bruno E. Correia, Michael M. Bronstein | Proteins' biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional sites, such as binding targets for potential drugs. Unfortunately though, the use of meshes as the underlying representation for protein structure has multiple drawbacks including the need to pre-compute the input features and mesh connectivities. This becomes a bottleneck for many important tasks in protein science. In this paper, we present a new framework for deep learning on protein structures that addresses these limitations. Among the key advantages of our method are the computation and sampling of the molecular surface on-the-fly from the underlying atomic point cloud and a novel efficient geometric convolutional layer. As a result, we are able to process large collections of proteins in an end-to-end fashion, taking as the sole input the raw 3D coordinates and chemical types of their atoms, eliminating the need for any hand-crafted pre-computed features. To showcase the performance of our approach, we test it on two tasks in the field of protein structural bioinformatics: the identification of interaction sites and the prediction of protein-protein interactions. On both tasks, we achieve state-of-the-art performance with much faster run times and fewer parameters than previous models. These results will considerably ease the deployment of deep learning methods in protein science and open the door for end-to-end differentiable approaches in protein modeling tasks such as function prediction and design. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sverrisson_Fast_End-to-End_Learning_on_Protein_Surfaces_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sverrisson_Fast_End-to-End_Learning_on_Protein_Surfaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sverrisson_Fast_End-to-End_Learning_on_Protein_Surfaces_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sverrisson_Fast_End-to-End_Learning_CVPR_2021_supplemental.pdf | null |
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules | Aisha Urooj, Hilde Kuehne, Kevin Duarte, Chuang Gan, Niels Lobo, Mubarak Shah | The problem of grounding VQA tasks has seen an increased attention in the research community recently, with most attempts usually focusing on solving this task by using pretrained object detectors. However, pre-trained object detectors require bounding box annotations for detecting relevant objects in the vocabulary, which may not always be feasible for real-life large-scale applications. In this paper, we focus on a more relaxed setting: the grounding of relevant visual entities in a weakly supervised manner by training on the VQA task alone. To address this problem, we propose a visual capsule module with a query-based selection mechanism of capsule features, that allows the model to focus on relevant regions based on the textual cues about visual information in the question. We show that integrating the proposed capsule module in existing VQA systems significantly improves their performance on the weakly supervised grounding task. Overall, we demonstrate the effectiveness of our approach on two state-of-the-art VQA systems, stacked NMN and MAC, on the CLEVR-Answers benchmark, our new evaluation set based on CLEVR scenes with ground truth bounding boxes for objects that are relevant for the correct answer, as well as on GQA, a real world VQA dataset with compositional questions. We show that the systems with the proposed capsule module consistently outperform the respective baseline systems in terms of answer grounding, while achieving comparable performance on VQA task. | https://openaccess.thecvf.com/content/CVPR2021/papers/Urooj_Found_a_Reason_for_me_Weakly-supervised_Grounded_Visual_Question_Answering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.04836 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Urooj_Found_a_Reason_for_me_Weakly-supervised_Grounded_Visual_Question_Answering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Urooj_Found_a_Reason_for_me_Weakly-supervised_Grounded_Visual_Question_Answering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Urooj_Found_a_Reason_CVPR_2021_supplemental.pdf | null |
Person Re-Identification Using Heterogeneous Local Graph Attention Networks | Zhong Zhang, Haijia Zhang, Shuang Liu | Recently, some methods have focused on learning local relation among parts of pedestrian images for person re-identification (Re-ID), as it offers powerful representation capabilities. However, they only provide the intra-local relation among parts within single pedestrian image and ignore the inter-local relation among parts from different images, which results in incomplete local relation information. In this paper, we propose a novel deep graph model named Heterogeneous Local Graph Attention Networks (HLGAT) to model the inter-local relation and the intra-local relation in the completed local graph, simultaneously. Specifically, we first construct the completed local graph using local features, and we resort to the attention mechanism to aggregate the local features in the learning process of inter-local relation and intra-local relation so as to emphasize the importance of different local features. As for the inter-local relation, we propose the attention regularization loss to constrain the attention weights based on the identities of local features in order to describe the inter-local relation accurately. As for the intra-local relation, we propose to inject the contextual information into the attention weights to consider structure information. Extensive experiments on Market-1501, CUHK03, DukeMTMC-reID and MSMT17 demonstrate that the proposed HLGAT outperforms the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Person_Re-Identification_Using_Heterogeneous_Local_Graph_Attention_Networks_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Person_Re-Identification_Using_Heterogeneous_Local_Graph_Attention_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Person_Re-Identification_Using_Heterogeneous_Local_Graph_Attention_Networks_CVPR_2021_paper.html | CVPR 2021 | null | null |
Recurrent Multi-View Alignment Network for Unsupervised Surface Registration | Wanquan Feng, Juyong Zhang, Hongrui Cai, Haofei Xu, Junhui Hou, Hujun Bao | Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data. In this paper, we resolve these two challenges simultaneously. First, we propose to represent the non-rigid transformation with a point-wise combination of several rigid transformations. This representation not only makes the solution space well-constrained but also enables our method to be solved iteratively with a recurrent framework, which greatly reduces the difficulty of learning. Second, we introduce a differentiable loss function that measures the 3D shape similarity on the projected multi-view 2D depth images so that our full framework can be trained end-to-end without ground truth supervision. Extensive experiments on several different datasets demonstrate that our proposed method outperforms the previous state-of-the-art by a large margin. | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Recurrent_Multi-View_Alignment_Network_for_Unsupervised_Surface_Registration_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12104 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Recurrent_Multi-View_Alignment_Network_for_Unsupervised_Surface_Registration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Recurrent_Multi-View_Alignment_Network_for_Unsupervised_Surface_Registration_CVPR_2021_paper.html | CVPR 2021 | null | null |
Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction | Sriram Narayanan, Ramin Moslemi, Francesco Pittaluga, Buyu Liu, Manmohan Chandraker | Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse hypotheses is under-studied as such objectives highly depend on their initialization for diversity. As our first contribution, we propose a novel Divide-And-Conquer (DAC) approach that acts as a better initialization technique to WTA objective, resulting in diverse outputs without any spurious modes. Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes. Our framework provides multi-agent trajectory outputs in a forward pass by capturing interactions through hypercolumn descriptors and incorporating scene information in the form of rasterized images and per-agent lane anchors. Experiments on synthetic and real data show that the proposed DAC captures the data distribution better compare to other WTA family of objectives. Further, we show that our ALAN approach provides on par or better performance with SOTA methods evaluated on Nuscenes urban driving benchmark. | https://openaccess.thecvf.com/content/CVPR2021/papers/Narayanan_Divide-and-Conquer_for_Lane-Aware_Diverse_Trajectory_Prediction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08277 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Narayanan_Divide-and-Conquer_for_Lane-Aware_Diverse_Trajectory_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Narayanan_Divide-and-Conquer_for_Lane-Aware_Diverse_Trajectory_Prediction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Narayanan_Divide-and-Conquer_for_Lane-Aware_CVPR_2021_supplemental.pdf | null |
Probabilistic 3D Human Shape and Pose Estimation From Multiple Unconstrained Images in the Wild | Akash Sengupta, Ignas Budvytis, Roberto Cipolla | This paper addresses the problem of 3D human body shape and pose estimation from RGB images. Recent progress in this field has focused on single images, video or multi-view images as inputs. In contrast, we propose a new task: shape and pose estimation from a group of multiple images of a human subject, without constraints on subject pose, camera viewpoint or background conditions between images in the group. Our solution to this task predicts distributions over SMPL body shape and pose parameters conditioned on the input images in the group. We probabilistically combine predicted body shape distributions from each image to obtain a final multi-image shape prediction. We show that the additional body shape information present in multi-image input groups improves 3D human shape estimation metrics compared to single-image inputs on the SSP-3D dataset and a private dataset of tape-measured humans. In addition, predicting distributions over 3D bodies allows us to quantify pose prediction uncertainty, which is useful when faced with challenging input images with significant occlusion. Our method demonstrates meaningful pose uncertainty on the 3DPW dataset and is competitive with the state-of-the-art in terms of pose estimation metrics. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sengupta_Probabilistic_3D_Human_Shape_and_Pose_Estimation_From_Multiple_Unconstrained_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.10978 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sengupta_Probabilistic_3D_Human_Shape_and_Pose_Estimation_From_Multiple_Unconstrained_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sengupta_Probabilistic_3D_Human_Shape_and_Pose_Estimation_From_Multiple_Unconstrained_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sengupta_Probabilistic_3D_Human_CVPR_2021_supplemental.pdf | null |
Weakly Supervised Instance Segmentation for Videos With Temporal Mask Consistency | Qing Liu, Vignesh Ramanathan, Dhruv Mahajan, Alan Yuille, Zhenheng Yang | Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions. We show that these issues can be better addressed by training with weakly labeled videos instead of images. In videos, motion and temporal consistency of predictions across frames provide complementary signals which can help segmentation. We are the first to explore the use of these video signals to tackle weakly supervised instance segmentation. We propose two ways to leverage this information in our model. First, we adapt inter-pixel relation network (IRN) to effectively incorporate motion information during training. Second, we introduce a new MaskConsist module, which addresses the problem of missing object instances by transferring stable predictions between neighboring frames during training. We demonstrate that both approaches together improve the instance segmentation metric AP50 on video frames of two datasets: Youtube-VIS and Cityscapes by 5% and 3% respectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Weakly_Supervised_Instance_Segmentation_for_Videos_With_Temporal_Mask_Consistency_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Weakly_Supervised_Instance_Segmentation_for_Videos_With_Temporal_Mask_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Weakly_Supervised_Instance_Segmentation_for_Videos_With_Temporal_Mask_Consistency_CVPR_2021_paper.html | CVPR 2021 | null | null |
Exploring Data-Efficient 3D Scene Understanding With Contrastive Scene Contexts | Ji Hou, Benjamin Graham, Matthias Niessner, Saining Xie | The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited; even given sufficient data, acquiring 3D labels (e.g. instance masks) requires intensive human labor. In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both point-level correspondences and spatial contexts in a scene. Our method achieves state-of-the-art results on a suite of benchmarks where training data or labels are scarce. Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary; and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Exploring_Data-Efficient_3D_Scene_Understanding_With_Contrastive_Scene_Contexts_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.09165 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Exploring_Data-Efficient_3D_Scene_Understanding_With_Contrastive_Scene_Contexts_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Exploring_Data-Efficient_3D_Scene_Understanding_With_Contrastive_Scene_Contexts_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hou_Exploring_Data-Efficient_3D_CVPR_2021_supplemental.pdf | null |
MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition | Ayan Kumar Bhunia, Shuvozit Ghose, Amandeep Kumar, Pinaki Nath Chowdhury, Aneeshan Sain, Yi-Zhe Song | Handwritten Text Recognition (HTR) remains a challenging problem to date, largely due to the varying writing styles that exist amongst us. Prior works however generally operate with the assumption that there is a limited number of styles, most of which have already been captured by existing datasets. In this paper, we take a completely different perspective -- we work on the assumption that there is always a new style that is drastically different, and that we will only have very limited data during testing to perform adaptation. This results in creates a commercially viable solution -- being exposed to the new style, the model has the best shot at adaptation, and the few-sample nature makes it practical to implement. We achieve this via a novel meta-learning framework which exploits additional new-writer data via a support set, and outputs a writer-adapted model via single gradient step update, all during inference. We discover and leverage on the important insight that there exists few key characters per writer that exhibit relatively larger style discrepancies. For that, we additionally propose to meta-learn instance specific weights for a character-wise cross-entropy loss, which is specifically designed to work with the sequential nature of text data. Our writer-adaptive MetaHTR framework can be easily implemented on the top of most state-of-the-art HTR models. Experiments show an average performance gain of 5-7% can be obtained by observing very few new style data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhunia_MetaHTR_Towards_Writer-Adaptive_Handwritten_Text_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01876 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bhunia_MetaHTR_Towards_Writer-Adaptive_Handwritten_Text_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bhunia_MetaHTR_Towards_Writer-Adaptive_Handwritten_Text_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhunia_MetaHTR_Towards_Writer-Adaptive_CVPR_2021_supplemental.pdf | null |
Learning To Reconstruct High Speed and High Dynamic Range Videos From Events | Yunhao Zou, Yinqiang Zheng, Tsuyoshi Takatani, Ying Fu | Event cameras are novel sensors that capture the dynamics of a scene asynchronously. Such cameras record event streams with much shorter response latency than images captured by conventional cameras, and are also highly sensitive to intensity change, which is brought by the triggering mechanism of events. On the basis of these two features, previous works attempt to reconstruct high speed and high dynamic range (HDR) videos from events. However, these works either suffer from unrealistic artifacts, or cannot provide sufficiently high frame rate. In this paper, we present a convolutional recurrent neural network which takes a sequence of neighboring events to reconstruct high speed HDR videos, and temporal consistency is well considered to facilitate the training process. In addition, we setup a prototype optical system to collect a real-world dataset with paired high speed HDR videos and event streams, which will be made publicly accessible for future researches in this field. Experimental results on both simulated and real scenes verify that our method can generate high speed HDR videos with high quality, and outperform the state-of-the-art reconstruction methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zou_Learning_To_Reconstruct_High_Speed_and_High_Dynamic_Range_Videos_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_Learning_To_Reconstruct_High_Speed_and_High_Dynamic_Range_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_Learning_To_Reconstruct_High_Speed_and_High_Dynamic_Range_Videos_CVPR_2021_paper.html | CVPR 2021 | null | null |
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS With Relationship Recovery | Tianyi Zhang, Jie Lin, Peng Hu, Bin Zhao, Mohamed M. Sabry Aly | Non-maximum Suppression (NMS) is an essential post-processing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines. MaxpoolNMS is introduced as a parallelizable alternative to GreedyNMS, which in turn enables faster speed than GreedyNMS at comparable accuracy. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of two-stage detectors like Faster-RCNN. There is a significant drop in accuracy when applying MaxpoolNMS at the final detection stage, due to the fact that MaxpoolNMS fails to approximate GreedyNMS precisely in terms of bounding box selection. In this paper, we propose a general, parallelizable and configurable approach PSRR-MaxpoolNMS, to completely replace GreedyNMS at all stages in all detectors. By introducing a simple Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module, our PSRR-MaxpoolNMS is able to approximate GreedyNMS more precisely than MaxpoolNMS. Comprehensive experiments show that our approach outperforms MaxpoolNMS by a large margin, and it is proven faster than GreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS provides a fully parallelizable solution for customized hardware design, which can be reused for accelerating NMS everywhere. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_PSRR-MaxpoolNMS_Pyramid_Shifted_MaxpoolNMS_With_Relationship_Recovery_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_PSRR-MaxpoolNMS_Pyramid_Shifted_MaxpoolNMS_With_Relationship_Recovery_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_PSRR-MaxpoolNMS_Pyramid_Shifted_MaxpoolNMS_With_Relationship_Recovery_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_PSRR-MaxpoolNMS_Pyramid_Shifted_CVPR_2021_supplemental.pdf | null |
Flow-Guided One-Shot Talking Face Generation With a High-Resolution Audio-Visual Dataset | Zhimeng Zhang, Lincheng Li, Yu Ding, Changjie Fan | One-shot talking face generation should synthesize high visual quality facial videos with reasonable animations of expression and head pose, and just utilize arbitrary driving audio and arbitrary single face image as the source. Current works fail to generate over 256 x 256 resolution realistic-looking videos due to the lack of an appropriate high-resolution audio-visual dataset, and the limitation of the sparse facial landmarks in providing poor expression details. To synthesize high-definition videos, we build a large in-the-wild high-resolution audio-visual dataset and propose a novel flow-guided talking face generation framework. The new dataset is collected from youtube and consists of about 16 hours 720P or 1080P videos. We leverage the facial 3D morphable model (3DMM) to split the framework into two cascaded modules instead of learning a direct mapping from audio to video. In the first module, we propose a novel animation generator to produce the movements of mouth, eyebrow and head pose simultaneously. In the second module, we transform animation into dense flow to provide more expression details and carefully design a novel flow-guided video generator to synthesize videos. Our method is able to produce high-definition videos and outperforms state-of-the-art works in objective and subjective comparisons. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Flow-Guided_One-Shot_Talking_Face_Generation_With_a_High-Resolution_Audio-Visual_Dataset_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Flow-Guided_One-Shot_Talking_Face_Generation_With_a_High-Resolution_Audio-Visual_Dataset_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Flow-Guided_One-Shot_Talking_Face_Generation_With_a_High-Resolution_Audio-Visual_Dataset_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Flow-Guided_One-Shot_Talking_CVPR_2021_supplemental.pdf | null |
VIGOR: Cross-View Image Geo-Localization Beyond One-to-One Retrieval | Sijie Zhu, Taojiannan Yang, Chen Chen | Cross-view image geo-localization aims to determine the locations of street-view query images by matching with GPS-tagged reference images from aerial view. Recent works have achieved surprisingly high retrieval accuracy on city-scale datasets. However, these results rely on the assumption that there exists a reference image exactly centered at the location of any query image, which is not applicable for practical scenarios. In this paper, we redefine this problem with a more realistic assumption that the query image can be arbitrary in the area of interest and the reference images are captured before the queries emerge. This assumption breaks the one-to-one retrieval setting of existing datasets as the queries and reference images are not perfectly aligned pairs, and there may be multiple reference images covering one query location. To bridge the gap between this realistic setting and existing datasets, we propose a new large-scale benchmark --VIGOR-- for cross-View Image Geo-localization beyond One-to-one Retrieval. We benchmark existing state-of-the-art methods and propose a novel end-to-end framework to localize the query in a coarse-to-fine manner. Apart from the image-level retrieval accuracy, we also evaluate the localization accuracy in terms of the actual distance (meters) using the raw GPS data. Extensive experiments are conducted under different application scenarios to validate the effectiveness of the proposed method. The results indicate that cross-view geo-localization in this realistic setting is still challenging, fostering new research in this direction. Our dataset and code will be publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_VIGOR_Cross-View_Image_Geo-Localization_Beyond_One-to-One_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12172 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_VIGOR_Cross-View_Image_Geo-Localization_Beyond_One-to-One_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_VIGOR_Cross-View_Image_Geo-Localization_Beyond_One-to-One_Retrieval_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_VIGOR_Cross-View_Image_CVPR_2021_supplemental.pdf | null |
D-NeRF: Neural Radiance Fields for Dynamic Scenes | Albert Pumarola, Enric Corona, Gerard Pons-Moll, Francesc Moreno-Noguer | Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF), which trains a deep network to map 5D input coordinates (representing spatial location and viewing direction) into a volume density and view-dependent emitted radiance. However, despite achieving an unprecedented level of photorealism on the generated images, NeRF is only applicable to static scenes, where the same spatial location can be queried from different images. In this paper we introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain, allowing to reconstruct and render novel images of objects under rigid and non-rigid motions. For this purpose we consider time as an additional input to the system, and split the learning process in two main stages: one that encodes the scene into a canonical space and another that maps this canonical representation into the deformed scene at a particular time. Both mappings are learned using fully-connected networks. Once the networks are trained, D-NeRF can render novel images, controlling both the camera view and the time variable, and thus, the object movement. We demonstrate the effectiveness of our approach on scenes with objects under rigid, articulated and non-rigid motions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pumarola_D-NeRF_Neural_Radiance_Fields_for_Dynamic_Scenes_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pumarola_D-NeRF_Neural_Radiance_Fields_for_Dynamic_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pumarola_D-NeRF_Neural_Radiance_Fields_for_Dynamic_Scenes_CVPR_2021_paper.html | CVPR 2021 | null | null |
Towards Unified Surgical Skill Assessment | Daochang Liu, Qiyue Li, Tingting Jiang, Yizhou Wang, Rulin Miao, Fei Shan, Ziyu Li | Surgical skills have a great influence on surgical safety and patients' well-being. Traditional assessment of surgical skills involves strenuous manual efforts, which lacks efficiency and repeatability. Therefore, we attempt to automatically predict how well the surgery is performed using the surgical video. In this paper, a unified multi-path framework for automatic surgical skill assessment is proposed, which takes care of multiple composing aspects of surgical skills, including surgical tool usage, intraoperative event pattern, and other skill proxies. The dependency relationships among these different aspects are specially modeled by a path dependency module in the framework. We conduct extensive experiments on the JIGSAWS dataset of simulated surgical tasks, and a new clinical dataset of real laparoscopic surgeries. The proposed framework achieves promising results on both datasets, with the state-of-the-art on the simulated dataset advanced from 0.71 Spearman's correlation to 0.80. It is also shown that combining multiple skill aspects yields better performance than relying on a single aspect. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Towards_Unified_Surgical_Skill_Assessment_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.01035 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Towards_Unified_Surgical_Skill_Assessment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Towards_Unified_Surgical_Skill_Assessment_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Towards_Unified_Surgical_CVPR_2021_supplemental.zip | null |
Read and Attend: Temporal Localisation in Sign Language Videos | Gul Varol, Liliane Momeni, Samuel Albanie, Triantafyllos Afouras, Andrew Zisserman | The objective of this work is to annotate sign instances across a broad vocabulary in continuous sign language. We train a Transformer model to ingest a continuous signing stream and output a sequence of written tokens on a large-scale collection of signing footage with weakly-aligned subtitles. We show that through this training it acquires the ability to attend to a large vocabulary of sign instances in the input sequence, enabling their localisation. Our contributions are as follows: (1) we demonstrate the ability to leverage large quantities of continuous signing videos with weakly-aligned subtitles to localise signs in continuous sign language; (2) we employ the learned attention to automatically generate hundreds of thousands of annotations for a large sign vocabulary; (3) we collect a set of 37K manually verified sign instances across a vocabulary of 950 sign classes to support our study of sign language recognition; (4) by training on the newly annotated data from our method, we outperform the prior state of the art on the BSL-1K sign language recognition benchmark. | https://openaccess.thecvf.com/content/CVPR2021/papers/Varol_Read_and_Attend_Temporal_Localisation_in_Sign_Language_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16481 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Varol_Read_and_Attend_Temporal_Localisation_in_Sign_Language_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Varol_Read_and_Attend_Temporal_Localisation_in_Sign_Language_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Varol_Read_and_Attend_CVPR_2021_supplemental.pdf | null |
ABMDRNet: Adaptive-Weighted Bi-Directional Modality Difference Reduction Network for RGB-T Semantic Segmentation | Qiang Zhang, Shenlu Zhao, Yongjiang Luo, Dingwen Zhang, Nianchang Huang, Jungong Han | Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images. Despite its importance, most existing RGB-T semantic segmentation models perform primitive fusion strategies, such as concatenation, element-wise summation and weighted summation, to fuse features from different modalities. These strategies, unfortunately, overlook the modality differences due to different imaging mechanisms, so that they suffer from the reduced discriminability of the fused features. To address such an issue, we propose, for the first time, the strategy of bridging-then-fusing, where the innovation lies in a novel Adaptive-weighted Bi-directional Modality Difference Reduction Network (ABMDRNet). Concretely, a Modality Difference Reduction and Fusion (MDRF) subnetwork is designed, which first employs a bi-directional image-to-image translation based method to reduce the modality differences between RGB features and thermal features, and then adaptively selects those discriminative multi-modality features for RGB-T semantic segmentation in a channel-wise weighted fusion way. Furthermore, considering the importance of contextual information in semantic segmentation, a Multi-Scale Spatial Context (MSC) module and a Multi-Scale Channel Context (MCC) module are proposed to exploit the interactions among multi-scale contextual information of cross-modality features together with their long-range dependencies along spatial and channel dimensions, respectively. Comprehensive experiments on MFNet dataset demonstrate that our method achieves new state-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_Modality_Difference_Reduction_Network_for_RGB-T_Semantic_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_Modality_Difference_Reduction_Network_for_RGB-T_Semantic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_Modality_Difference_Reduction_Network_for_RGB-T_Semantic_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_CVPR_2021_supplemental.pdf | null |
Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation | Ryuhei Hamaguchi, Yasutaka Furukawa, Masaki Onishi, Ken Sakurada | This paper proposes a novel heterogeneous grid convolution that builds a graph-based image representation by exploiting heterogeneity in the image content, enabling adaptive, efficient, and controllable computations in a convolutional architecture. More concretely, the approach builds a data-adaptive graph structure from a convolutional layer by a differentiable clustering method, pools features to the graph, performs a novel direction-aware graph convolution, and unpool features back to the convolutional layer. By using the developed module, the paper proposes heterogeneous grid convolutional networks, highly efficient yet strong extension of existing architectures. We have evaluated the proposed approach on four image understanding tasks, semantic segmentation, object localization, road extraction, and salient object detection. The proposed method is effective on three of the four tasks. Especially, the method outperforms a strong baseline with more than 90% reduction in floating-point operations for semantic segmentation, and achieves the state-of-the-art result for road extraction. We will share our code, model, and data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hamaguchi_Heterogeneous_Grid_Convolution_for_Adaptive_Efficient_and_Controllable_Computation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11176 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hamaguchi_Heterogeneous_Grid_Convolution_for_Adaptive_Efficient_and_Controllable_Computation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hamaguchi_Heterogeneous_Grid_Convolution_for_Adaptive_Efficient_and_Controllable_Computation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hamaguchi_Heterogeneous_Grid_Convolution_CVPR_2021_supplemental.pdf | null |
Learning a Facial Expression Embedding Disentangled From Identity | Wei Zhang, Xianpeng Ji, Keyu Chen, Yu Ding, Changjie Fan | The facial expression analysis requires a compact and identity-ignored expression representation. In this paper, we model the expression as the deviation from the identity by a subtraction operation, extracting a continuous and identity-invariant expression embedding. We propose a Deviation Learning Network (DLN) with a pseudo-siamese structure to extract the deviation feature vector. To reduce the optimization difficulty caused by additional fully connection layers, DLN directly provides high-order polynomial to nonlinearly project the high-dimensional feature to a low-dimensional manifold. Taking label noise into account, we add a crowd layer to DLN for robust embedding extraction. Also, to achieve a more compact representation, we use hierarchical annotation for data augmentation. We evaluate our facial expression embedding on the FEC validation set. The quantitative results prove that we achieve the state-of-the-art, both in terms of fine-grained and identity-invariant property. We further conduct extensive experiments to show that our expression embedding is of high quality for emotion recognition, image retrieval, and face manipulation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_a_Facial_Expression_Embedding_Disentangled_From_Identity_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_a_Facial_Expression_Embedding_Disentangled_From_Identity_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_a_Facial_Expression_Embedding_Disentangled_From_Identity_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Learning_a_Facial_CVPR_2021_supplemental.pdf | null |
Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization | Jiaru Zhang, Yang Hua, Zhengui Xue, Tao Song, Chengyu Zheng, Ruhui Ma, Haibing Guan | Bayesian neural networks have been widely used in many applications because of the distinctive probabilistic representation framework. Even though Bayesian neural networks have been found more robust to adversarial attacks compared with vanilla neural networks, their ability to deal with adversarial noises in practice is still limited. In this paper, we propose Spectral Expectation Bound Regularization (SEBR) to enhance the robustness of Bayesian neural networks. Our theoretical analysis reveals that training with SEBR improves the robustness to adversarial noises. We also prove that training with SEBR can reduce the epistemic uncertainty of the model and hence it can make the model more confident with the predictions, which verifies the robustness of the model from another point of view. Experiments on multiple Bayesian neural network structures and different adversarial attacks validate the correctness of the theoretical findings and the effectiveness of the proposed approach. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Robust_Bayesian_Neural_Networks_by_Spectral_Expectation_Bound_Regularization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Robust_Bayesian_Neural_Networks_by_Spectral_Expectation_Bound_Regularization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Robust_Bayesian_Neural_Networks_by_Spectral_Expectation_Bound_Regularization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Robust_Bayesian_Neural_CVPR_2021_supplemental.pdf | null |
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression | Wanhua Li, Xiaoke Huang, Jiwen Lu, Jianjiang Feng, Jie Zhou | Uncertainty is the only certainty there is. Modeling data uncertainty is essential for regression, especially in unconstrained settings. Traditionally the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. On the other hand, classification based regression and ranking based solutions are more popular in practice while the direct regression methods suffer from the limited performance. How to model the uncertainty within the present-day technologies for regression remains an open issue. In this paper, we propose to learn probabilistic ordinal embeddings which represent each data as a multivariate Gaussian distribution rather than a deterministic point in the latent space. An ordinal distribution constraint is proposed to exploit the ordinal nature of regression. Our probabilistic ordinal embeddings can be integrated into popular regression approaches and empower them with the ability of uncertainty estimation. Experimental results show that our approach achieves competitive performance. Code is available at https://github.com/Li-Wanhua/POEs. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Learning_Probabilistic_Ordinal_Embeddings_for_Uncertainty-Aware_Regression_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13629 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Learning_Probabilistic_Ordinal_Embeddings_for_Uncertainty-Aware_Regression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Learning_Probabilistic_Ordinal_Embeddings_for_Uncertainty-Aware_Regression_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Learning_Probabilistic_Ordinal_CVPR_2021_supplemental.pdf | null |
StyleMix: Separating Content and Style for Enhanced Data Augmentation | Minui Hong, Jinwoo Choi, Gunhee Kim | In spite of the great success of deep neural networks for many challenging classification tasks, the learned networks are vulnerable to overfitting and adversarial attacks. Recently, mixup based augmentation methods have been actively studied as one practical remedy for these drawbacks. However, these approaches do not distinguish between the content and style features of the image, but simply mix or cut-and-paste the image. We propose StyleMix and StyleCutMix as the first mixup method that separately manipulates the content and style information of input image pairs. By carefully mixing up the content and style of images, we can create more abundant and robust samples, which eventually enhance the generalization of the model training. We also develop an automatic scheme to decide the degree of style mixing according to the pair's class distance, to prevent messy mixed images from too differently styled pairs. Our experiments on CIFAR-100, CIFAR-10, and ImageNet datasets show that StyleMix achieves comparable performance to state of the art mixup methods and learns more robust classifiers to adversarial attacks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_StyleMix_Separating_Content_and_Style_for_Enhanced_Data_Augmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_StyleMix_Separating_Content_and_Style_for_Enhanced_Data_Augmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_StyleMix_Separating_Content_and_Style_for_Enhanced_Data_Augmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_StyleMix_Separating_Content_CVPR_2021_supplemental.pdf | null |
Kaleido-BERT: Vision-Language Pre-Training on Fashion Domain | Mingchen Zhuge, Dehong Gao, Deng-Ping Fan, Linbo Jin, Ben Chen, Haoming Zhou, Minghui Qiu, Ling Shao | We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers. In contrast to random masking strategy of recent VL models, we design alignment guided masking to jointly focus more on image-text semantic relations. To this end, we carry out five novel tasks, i.e., rotation, jigsaw, camouflage, grey-to-color, and blank-to-color for self-supervised VL pre-training at patches of different scale. Kaleido-BERT is conceptually simple and easy to extend to the existing BERT framework, it attains new state-of-the-art results by large margins on four downstream tasks, including text retrieval (R@1: 4.03% absolute improvement), image retrieval (R@1: 7.13% abs imv.), category recognition (ACC: 3.28% abs imv.), and fashion captioning (Bleu4: 1.2 abs imv.). We validate the efficiency of Kaleido-BERT on a wide range of e-commerical websites, demonstrating its broader potential in real-world applications. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhuge_Kaleido-BERT_Vision-Language_Pre-Training_on_Fashion_Domain_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhuge_Kaleido-BERT_Vision-Language_Pre-Training_on_Fashion_Domain_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhuge_Kaleido-BERT_Vision-Language_Pre-Training_on_Fashion_Domain_CVPR_2021_paper.html | CVPR 2021 | null | null |
Co-Grounding Networks With Semantic Attention for Referring Expression Comprehension in Videos | Sijie Song, Xudong Lin, Jiaying Liu, Zongming Guo, Shih-Fu Chang | In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics. Unlike previous methods which solve the problem in multiple stages (i.e., tracking, proposal-based matching), we tackle the problem from a novel perspective, co-grounding, with an elegant one-stage framework. We enhance the single-frame grounding accuracy by semantic attention learning and improve the cross-frame grounding consistency with co-grounding feature learning. Semantic attention learning explicitly parses referring cues in different attributes to reduce the ambiguity in the complex expression. Co-grounding feature learning boosts visual feature representations by integrating temporal correlation to reduce the ambiguity caused by scene dynamics. Experiment results demonstrate the superiority of our framework on the video grounding datasets VID and OTB in generating accurate and stable results across frames. Our model is also applicable to referring expression comprehension in images, illustrated by the improved performance on the RefCOCO dataset. Our project is available at https://sijiesong.github.io/co-grounding. | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Co-Grounding_Networks_With_Semantic_Attention_for_Referring_Expression_Comprehension_in_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12346 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Co-Grounding_Networks_With_Semantic_Attention_for_Referring_Expression_Comprehension_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Co-Grounding_Networks_With_Semantic_Attention_for_Referring_Expression_Comprehension_in_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Co-Grounding_Networks_With_CVPR_2021_supplemental.pdf | null |
Binary Graph Neural Networks | Mehdi Bahri, Gaetan Bahl, Stefanos Zafeiriou | Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. As they generalize the operations of classical CNNs on grids to arbitrary topologies, GNNs also bring much of the implementation challenges of their Euclidean counterparts. Model size, memory footprint, and energy consumption are common concerns for many real-world applications. Network binarization allocates a single bit to parameters and activations, thus dramatically reducing the memory requirements (up to 32x compared to single-precision floating-point numbers) and maximizing the benefits of fast SIMD instructions on modern hardware for measurable speedups. However, in spite of the large body of work on binarization for classical CNNs, this area remains largely unexplored in geometric deep learning. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks. In particular, we present the first dynamic graph neural network in Hamming space, able to leverage efficient k-NN search on binary vectors to speed-up the construction of the dynamic graph. We further verify that the binary models offer significant savings on embedded devices. Our code is publicly available on Github. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bahri_Binary_Graph_Neural_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.15823 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bahri_Binary_Graph_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bahri_Binary_Graph_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bahri_Binary_Graph_Neural_CVPR_2021_supplemental.pdf | null |
3D CNNs With Adaptive Temporal Feature Resolutions | Mohsen Fayyaz, Emad Bahrami, Ali Diba, Mehdi Noroozi, Ehsan Adeli, Luc Van Gool, Jurgen Gall | While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips. In this work, we therefore introduce a differentiable Similarity Guided Sampling (SGS) module, which can be plugged into any existing 3D CNN architecture. SGS empowers 3D CNNs by learning the similarity of temporal features and grouping similar features together. As a result, the temporal feature resolution is not anymore static but it varies for each input video clip. By integrating SGS as an additional layer within current 3D CNNs, we can convert them into much more efficient 3D CNNs with adaptive temporal feature resolutions (ATFR). Our evaluations show that the proposed module improves the state-of-the-art by reducing the computational cost (GFLOPs) by half while preserving or even improving the accuracy. We evaluate our module by adding it to multiple state-of-the-art 3D CNNs on various datasets such as Kinetics-600, Kinetics-400, mini-Kinetics, Something-Something V2, UCF101, and HMDB51. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fayyaz_3D_CNNs_With_Adaptive_Temporal_Feature_Resolutions_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08652 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fayyaz_3D_CNNs_With_Adaptive_Temporal_Feature_Resolutions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fayyaz_3D_CNNs_With_Adaptive_Temporal_Feature_Resolutions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fayyaz_3D_CNNs_With_CVPR_2021_supplemental.pdf | null |
Space-Time Neural Irradiance Fields for Free-Viewpoint Video | Wenqi Xian, Jia-Bin Huang, Johannes Kopf, Changil Kim | We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video. Our learned representation enables free-viewpoint rendering of the input video. Our method builds upon recent advances in implicit representations. Learning a spatiotemporal irradiance field from a single video poses significant challenges because the video contains only one observation of the scene at any point in time. The 3D geometry of a scene can be legitimately represented in numerous ways since varying geometry (motion) can be explained with varying appearance and vice versa. We address this ambiguity by constraining the time-varying geometry of our dynamic scene representation using the scene depth estimated from video depth estimation methods, aggregating contents from individual frames into a single global representation. We provide an extensive quantitative evaluation and demonstrate compelling free-viewpoint rendering results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xian_Space-Time_Neural_Irradiance_Fields_for_Free-Viewpoint_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12950 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xian_Space-Time_Neural_Irradiance_Fields_for_Free-Viewpoint_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xian_Space-Time_Neural_Irradiance_Fields_for_Free-Viewpoint_Video_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xian_Space-Time_Neural_Irradiance_CVPR_2021_supplemental.pdf | null |
AutoDO: Robust AutoAugment for Biased Data With Label Noise via Scalable Probabilistic Implicit Differentiation | Denis Gudovskiy, Luca Rigazio, Shun Ishizaka, Kazuki Kozuka, Sotaro Tsukizawa | AutoAugment has sparked an interest in automated augmentation methods for deep learning models. These methods estimate image transformation policies for train data that improve generalization to test data. While recent papers evolved in the direction of decreasing policy search complexity, we show that those methods are not robust when applied to biased and noisy data. To overcome these limitations, we reformulate AutoAugment as a generalized automated dataset optimization (AutoDO) task that minimizes the distribution shift between test data and distorted train dataset. In our AutoDO model, we explicitly estimate a set of per-point hyperparameters to flexibly change distribution of train data. In particular, we include hyperparameters for augmentation, loss weights, and soft-labels that are jointly estimated using implicit differentiation. We develop a theoretical probabilistic interpretation of this framework using Fisher information and show that its complexity scales linearly with the dataset size. Our experiments on SVHN, CIFAR-10/100, and ImageNet classification show up to 9.3% improvement for biased datasets with label noise compared to prior methods and, importantly, up to 36.6% gain for underrepresented SVHN classes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gudovskiy_AutoDO_Robust_AutoAugment_for_Biased_Data_With_Label_Noise_via_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05863 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gudovskiy_AutoDO_Robust_AutoAugment_for_Biased_Data_With_Label_Noise_via_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gudovskiy_AutoDO_Robust_AutoAugment_for_Biased_Data_With_Label_Noise_via_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gudovskiy_AutoDO_Robust_AutoAugment_CVPR_2021_supplemental.pdf | null |
Multiple Instance Active Learning for Object Detection | Tianning Yuan, Fang Wan, Mengying Fu, Jianzhuang Liu, Songcen Xu, Xiangyang Ji, Qixiang Ye | Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection. In this paper, we propose Multiple Instance Active Object Detection (MI-AOD), to select the most informative images for detector training by observing instance-level uncertainty. MI-AOD defines an instance uncertainty learning module, which leverages the discrepancy of two adversarial instance classifiers trained on the labeled set to predict instance uncertainty of the unlabeled set. MI-AOD treats unlabeled images as instance bags and feature anchors in images as instances, and estimates the image uncertainty by re-weighting instances in a multiple instance learning (MIL) fashion. Iterative instance uncertainty learning and re-weighting facilitate suppressing noisy instances, toward bridging the gap between instance uncertainty and image-level uncertainty. Experiments validate that MI-AOD sets a solid baseline for instance-level active learning. On commonly used object detection datasets, MI-AOD outperforms state-of-the-art methods with significant margins, particularly when the labeled sets are small. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02324 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Forecasting Irreversible Disease via Progression Learning | Botong Wu, Sijie Ren, Jing Li, Xinwei Sun, Shi-Ming Li, Yizhou Wang | Forecasting Parapapillary atrophy (PPA), i.e., a symptom related to most irreversible eye diseases, provides an alarm for implementing an intervention to slow down the disease progression at early stage. A key question for this forecast is: how to fully utilize the historical data (e.g., retinal image) up to the current stage for future disease prediction? In this paper, we provide an answer with a novel framework, namely Disease Forecast via Progression Learning (DFPL), which exploits the irreversibility prior (i.e., cannot be reversed once diagnosed). Specifically, based on this prior, we decompose two factors that contribute to the prediction of the future disease: i) the current disease label given the data (retinal image, clinical attributes) at present and ii) the future disease label given the progression of the retinal images that from the current to the future. To model these two factors, we introduce the current and progression predictors in DFPL, respectively. In order to account for the degree of progression of the disease, we propose a temporal generative model to accurately generate the future image and compare it with the current one to get a residual image. The generative model is implemented by a recurrent neural network, in order to exploit the dependency of the historical data. To verify our approach, we apply it to a PPA in-house dataset and it yields a significant improvement (e.g., 4.48% of accuracy; 3.45% of AUC) over others. Besides, our generative model can accurately localize the disease-related regions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Forecasting_Irreversible_Disease_via_Progression_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11107 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Forecasting_Irreversible_Disease_via_Progression_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Forecasting_Irreversible_Disease_via_Progression_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Forecasting_Irreversible_Disease_CVPR_2021_supplemental.zip | null |
Understanding the Robustness of Skeleton-Based Action Recognition Under Adversarial Attack | He Wang, Feixiang He, Zhexi Peng, Tianjia Shao, Yong-Liang Yang, Kun Zhou, David Hogg | Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we examine the robustness of state-of-the-art action recognizers against adversarial attack, which has been rarely investigated so far. To this end, we propose a new method to attack action recognizers which rely on the 3D skeletal motion. Our method involves an innovative perceptual loss which ensures the imperceptibility of the attack. Empirical studies demonstrate that our method is effective in both white-box and black-box scenarios. Its generalizability is evidenced on a variety of action recognizers and datasets. Its versatility is shown in different attacking strategies. Its deceitfulness is proven in extensive perceptual studies. Our method shows that adversarial attack on 3D skeletal motions, one type of time-series data, is significantly different from traditional adversarial attack problems. Its success raises serious concern on the robustness of action recognizers and provides insights on potential improvements. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Understanding_the_Robustness_of_Skeleton-Based_Action_Recognition_Under_Adversarial_Attack_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05347 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Understanding_the_Robustness_of_Skeleton-Based_Action_Recognition_Under_Adversarial_Attack_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Understanding_the_Robustness_of_Skeleton-Based_Action_Recognition_Under_Adversarial_Attack_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Understanding_the_Robustness_CVPR_2021_supplemental.pdf | null |
Learning Invariant Representations and Risks for Semi-Supervised Domain Adaptation | Bo Li, Yezhen Wang, Shanghang Zhang, Dongsheng Li, Kurt Keutzer, Trevor Darrell, Han Zhao | The success of supervised learning crucially hinges on the assumption that training data matches test data, which rarely holds in practice due to potential distribution shift. In light of this, most existing methods for unsupervised domain adaptation focus on achieving domain-invariant representations and small source domain error. However, recent works have shown that this is not sufficient to guarantee good generalization on target domain and in fact is provably detrimental under label distribution shift. Furthermore, in many real-world applications it is often feasible to obtain a small amount of labeled data from the target domain and use them to facilitate model training with source data. Inspired by the above observations, in this paper we propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA). To start with, we first give a finite sample bound for both classification and regression problems under Semi-DA. The bound suggests a principled way for target generalization by aligning both the marginal and conditional distributions across domains in feature space. Motivated by this, we then introduce our LIRR algorithm for jointly Learning Invariant Representations and Risks. Finally, we conduct extensive experiments on both classification and regression tasks to demonstrate the effectiveness of LIRR. Compared with methods that only learn invariant representations or invariant risks, LIRR achieves significant improvements. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Learning_Invariant_Representations_and_Risks_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.04647 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Learning_Invariant_Representations_and_Risks_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Learning_Invariant_Representations_and_Risks_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Learning_Invariant_Representations_CVPR_2021_supplemental.pdf | null |
Cross-MPI: Cross-Scale Stereo for Image Super-Resolution Using Multiplane Images | Yuemei Zhou, Gaochang Wu, Ying Fu, Kun Li, Yebin Liu | Various combinations of cameras enrich computational photography, among which reference-based superresolution (RefSR) plays a critical role in multiscale imaging systems. However, existing RefSR approaches fail to accomplish high-fidelity super-resolution under a large resolution gap, e.g., 8x upscaling, due to the lower consideration of the underlying scene structure. In this paper, we aim to solve the RefSR problem in actual multiscale camera systems inspired by multiplane image (MPI) representation. Specifically, we propose Cross-MPI, an end-to-end RefSR network composed of a novel plane-aware attention-based MPI mechanism, a multiscale guided upsampling module as well as a super-resolution (SR) synthesis and fusion module. Instead of using a direct and exhaustive matching between the cross-scale stereo, the proposed plane-aware attention mechanism fully utilizes the concealed scene structure for efficient attention-based correspondence searching. Further combined with a gentle coarse-to-fine guided upsampling strategy, the proposed Cross-MPI can achieve a robust and accurate detail transmission. Experimental results on both digitally synthesized and optical zoom cross-scale data show that the Cross-MPI framework can achieve superior performance against the existing RefSR methods and is a real fit for actual multiscale camera systems even with large-scale differences. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Cross-MPI_Cross-Scale_Stereo_for_Image_Super-Resolution_Using_Multiplane_Images_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Cross-MPI_Cross-Scale_Stereo_for_Image_Super-Resolution_Using_Multiplane_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Cross-MPI_Cross-Scale_Stereo_for_Image_Super-Resolution_Using_Multiplane_Images_CVPR_2021_paper.html | CVPR 2021 | null | null |
Neural Cellular Automata Manifold | Alejandro Hernandez, Armand Vilalta, Francesc Moreno-Noguer | Very recently, the Neural Cellular Automata (NCA) has been proposed to simulate the morphogenesis process with deep networks. NCA learns to grow an image starting from a fixed single pixel. In this work, we show that the neural network (NN) architecture of the NCA can be encapsulated in a larger NN. This allows us to propose a new model that encodes a manifold of NCA, each of them capable of generating a distinct image. Therefore, we are effectively learning an embedding space of CA, which shows generalization capabilities. We accomplish this by introducing dynamic convolutions inside an Auto-Encoder architecture, for the first time used to join two different sources of information, the encoding and cell's environment information. In biological terms, our approach would play the role of the transcription factors, modulating the mapping of genes into specific proteins that drive cellular differentiation, which occurs right before the morphogenesis. We thoroughly evaluate our approach in a dataset of synthetic emojis and also in real images of CIFAR-10. Our model introduces a general-purpose network, which can be used in a broad range of problems beyond image generation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hernandez_Neural_Cellular_Automata_Manifold_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hernandez_Neural_Cellular_Automata_Manifold_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hernandez_Neural_Cellular_Automata_Manifold_CVPR_2021_paper.html | CVPR 2021 | null | null |
Few-Shot Transformation of Common Actions Into Time and Space | Pengwan Yang, Pascal Mettes, Cees G. M. Snoek | This paper introduces the task of few-shot common action localization in time and space. Given a few trimmed support videos containing the same but unknown action, we strive for spatio-temporal localization of that action in a long untrimmed query video. We do not require any class labels, interval bounds, or bounding boxes. To address this challenging task, we introduce a novel few-shot transformer architecture with a dedicated encoder-decoder structure optimized for joint commonality learning and localization prediction, without the need for proposals. Experiments on our reorganizations of the AVA and UCF101-24 datasets show the effectiveness of our approach for few-shot common action localization, even when the support videos are noisy. Although we are not specifically designed for common localization in time only, we also compare favorably against the few-shot and one-shot state-of-the-art in this setting. Lastly, we demonstrate that the few-shot transformer is easily extended to common action localization per pixel. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Few-Shot_Transformation_of_Common_Actions_Into_Time_and_Space_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02439 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Few-Shot_Transformation_of_Common_Actions_Into_Time_and_Space_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Few-Shot_Transformation_of_Common_Actions_Into_Time_and_Space_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Few-Shot_Transformation_of_CVPR_2021_supplemental.pdf | null |
MultiLink: Multi-Class Structure Recovery via Agglomerative Clustering and Model Selection | Luca Magri, Filippo Leveni, Giacomo Boracchi | We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defined by a mixture of underlying parametric models (e.g. planes and cylinders, homographies and fundamental matrices), and we tackle the robust fitting problem by preference analysis and clustering. We present a new algorithm, termed MultiLink, that simultaneously deals with multiple classes of models. MultiLink wisely combines on-the-fly model fitting and model selection in a novel linkage scheme that determines whether two clusters are to be merged. The resulting method features many practical advantages with respect to methods based on preference analysis, being faster, less sensitive to the inlier threshold, and able to compensate limitations deriving from hypotheses sampling. Experiments on several public datasets demonstrate that MultiLink favorably compares with state of the art alternatives, both in multi-class and single-class problems. Code is publicly made available for download. | https://openaccess.thecvf.com/content/CVPR2021/papers/Magri_MultiLink_Multi-Class_Structure_Recovery_via_Agglomerative_Clustering_and_Model_Selection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Magri_MultiLink_Multi-Class_Structure_Recovery_via_Agglomerative_Clustering_and_Model_Selection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Magri_MultiLink_Multi-Class_Structure_Recovery_via_Agglomerative_Clustering_and_Model_Selection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Meta Pseudo Labels | Hieu Pham, Zihang Dai, Qizhe Xie, Quoc V. Le | We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the existing state-of-the-art. Like Pseudo Labels, Meta Pseudo Labels has a teacher network to generate pseudo labels on unlabeled data to teach a student network. However, unlike Pseudo Labels where the teacher is kept fixed, in Meta Pseudo Labels, the teacher is constantly adapted by the feedback of how well the student performs on the labeled dataset. As a result, the teacher generates better pseudo labels to teach the student. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pham_Meta_Pseudo_Labels_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.10580 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pham_Meta_Pseudo_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pham_Meta_Pseudo_Labels_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pham_Meta_Pseudo_Labels_CVPR_2021_supplemental.pdf | null |
SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction | Liushuai Shi, Le Wang, Chengjiang Long, Sanping Zhou, Mo Zhou, Zhenxing Niu, Gang Hua | Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very challenging due to complex interactions between pedestrians. However, previous works based on dense undirected interaction suffer from modeling superfluous interactions and neglect of trajectory motion tendency, and thus inevitably result in a considerable deviance from the reality. To cope with these issues, we present a Sparse Graph Convolution Network (SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. Meanwhile, we use a sparse directed temporal graph to model the motion tendency, thus to facilitate the prediction based on the observed direction. Finally, parameters of a bi-Gaussian distribution for trajectory prediction are estimated by fusing the above two sparse graphs. We evaluate our proposed method on the ETH and UCY datasets, and the experimental results show our method outperforms comparative state-of-the-art methods by 9% in Average Displacement Error (ADE) and 13% in Final Displacement Error (FDE). Notably, visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01528 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_SGCN_Sparse_Graph_CVPR_2021_supplemental.pdf | null |
Depth Completion Using Plane-Residual Representation | Byeong-Uk Lee, Kyunghyun Lee, In So Kweon | The basic framework of depth completion is to predict a pixel-wise dense depth map using very sparse input data. In this paper, we try to solve this problem in a more effective way, by reformulating the regression-based depth estimation problem into a combination of depth plane classification and residual regression. Our proposed approach is to initially densify sparse depth information by figuring out which plane a pixel should lie among a number of discretized depth planes, and then calculate the final depth value by predicting the distance from the specified plane. This will help the network to lessen the burden of directly regressing the absolute depth information from none, and to effectively obtain more accurate depth prediction result with less computation power and inference time. To do so, we firstly introduce a novel way of interpreting depth information with the closest depth plane label p and a residual value r, as we call it, Plane-Residual (PR) representation. We also propose a depth completion network utilizing PR representation consisting of a shared encoder and two decoders, where one classifies the pixel's depth plane label, while the other one regresses the normalized distance from the classified depth plane. By interpreting depth information in PR representation and using our corresponding depth completion network, we were able to acquire improved depth completion performance with faster computation, compared to previous approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Depth_Completion_Using_Plane-Residual_Representation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07350 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Depth_Completion_Using_Plane-Residual_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Depth_Completion_Using_Plane-Residual_Representation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise | Xiangyu Rui, Xiangyong Cao, Qi Xie, Zongsheng Yue, Qian Zhao, Deyu Meng | A general approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements. Such weighting scheme, however, largely depends on the prior understanding or subjective distribution assumption on HSI noises, making them easily biased to complicated real noises, and hardly generalizable to diverse practical scenarios. Against this issue, this paper proposes a new scheme aiming to capture general weighting principle in a data-driven manner. Specifically, such weighting principle is delivered by an explicit function, called hyperweight-net (HWnet), mapping from an input noisy image to its properly imposed weights. A Bayesian framework, as well as a variational inference algorithm, for inferring HWnet parameters is elaborately designed, expecting to extract the latent weighting rule for general diverse and complicated noisy HSIs. Comprehensive experiments substantiate that the learned HWnet can be not only finely generalized to different noise types from those used in training, but also effectively transferred to other weighted models for the issue. Besides, as a sounder guidance, HWnet can help to more faithfully and robustly achieve deep hyperspectral prior(DHP). Specially, the extracted weights by HWnet are verified to be able to effectively capture complex noise knowledge underlying input HSI, revealing its working insight in experiments. | https://openaccess.thecvf.com/content/CVPR2021/papers/Rui_Learning_an_Explicit_Weighting_Scheme_for_Adapting_Complex_HSI_Noise_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Rui_Learning_an_Explicit_Weighting_Scheme_for_Adapting_Complex_HSI_Noise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Rui_Learning_an_Explicit_Weighting_Scheme_for_Adapting_Complex_HSI_Noise_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rui_Learning_an_Explicit_CVPR_2021_supplemental.pdf | null |
Neural Parts: Learning Expressive 3D Shape Abstractions With Invertible Neural Networks | Despoina Paschalidou, Angelos Katharopoulos, Andreas Geiger, Sanja Fidler | Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these methods fail to accurately reconstruct 3D shapes using a small number of primitives/parts. We address the trade-off between reconstruction quality and number of parts with Neural Parts, a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) which implements homeomorphic mappings between a sphere and the target object. The INN allows us to compute the inverse mapping of the homeomorphism, which in turn, enables the efficient computation of both the implicit surface function of a primitive and its mesh, without any additional post-processing. Our model learns to parse 3D objects into semantically consistent part arrangements without any part-level supervision.Evaluations on ShapeNet, D-FAUST and FreiHAND demonstrate that our primitives can capture complex geometries and thus simultaneously achieve geometrically accurate as well as interpretable reconstructions using an order of magnitude fewer primitives than state-of-the-art shape abstraction methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Paschalidou_Neural_Parts_Learning_Expressive_3D_Shape_Abstractions_With_Invertible_Neural_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.10429 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Paschalidou_Neural_Parts_Learning_Expressive_3D_Shape_Abstractions_With_Invertible_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Paschalidou_Neural_Parts_Learning_Expressive_3D_Shape_Abstractions_With_Invertible_Neural_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Paschalidou_Neural_Parts_Learning_CVPR_2021_supplemental.pdf | null |
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds | Yi Wei, Ziyi Wang, Yongming Rao, Jiwen Lu, Jie Zhou | In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in the 3D space, where all-pairs correlations play important roles in scene flow estimation. To tackle this problem, we present point-voxel correlation fields, which capture both local and long-range dependencies of point pairs. To capture point-based correlations, we adopt the K-Nearest Neighbors search that preserves fine-grained information in the local region. By voxelizing point clouds in a multi-scale manner, we construct pyramid correlation voxels to model long-range correspondences. Integrating these two types of correlations, our PV-RAFT makes use of all-pairs relations to handle both small and large displacements. We evaluate the proposed method on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Experimental results show that PV-RAFT outperforms state-of-the-art methods by remarkable margins. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_PV-RAFT_Point-Voxel_Correlation_Fields_for_Scene_Flow_Estimation_of_Point_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_PV-RAFT_Point-Voxel_Correlation_Fields_for_Scene_Flow_Estimation_of_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_PV-RAFT_Point-Voxel_Correlation_Fields_for_Scene_Flow_Estimation_of_Point_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_PV-RAFT_Point-Voxel_Correlation_CVPR_2021_supplemental.pdf | null |
Improving the Efficiency and Robustness of Deepfakes Detection Through Precise Geometric Features | Zekun Sun, Yujie Han, Zeyu Hua, Na Ruan, Weijia Jia | Deepfakes is a branch of malicious techniques that transplant a target face to the original one in videos, resulting in serious problems such as infringement of copyright, confusion of information, or even public panic. Previous efforts for Deepfakes videos detection mainly focused on appearance features, which have a risk of being bypassed by sophisticated manipulation, also resulting in high model complexity and sensitiveness to noise. Besides, how to mine the temporal features of manipulated videos and exploit them is still an open question. We propose an efficient and robust framework named LRNet for detecting Deepfakes videos through temporal modeling on precise geometric features. A novel calibration module is devised to enhance the precision of geometric features, making it more discriminative, and a two-stream Recurrent Neural Network (RNN) is constructed for sufficient exploitation of temporal features. Compared to previous methods, our proposed method is lighter-weighted and easier to train. Moreover, our method has shown robustness in detecting highly compressed or noise corrupted videos. Our model achieved 0.999 AUC on FaceForensics++ dataset. Meanwhile, it has a graceful decline in performance (-0.042 AUC) when faced with highly compressed videos. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Improving_the_Efficiency_and_Robustness_of_Deepfakes_Detection_Through_Precise_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04480 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Improving_the_Efficiency_and_Robustness_of_Deepfakes_Detection_Through_Precise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Improving_the_Efficiency_and_Robustness_of_Deepfakes_Detection_Through_Precise_CVPR_2021_paper.html | CVPR 2021 | null | null |
Sketch2Model: View-Aware 3D Modeling From Single Free-Hand Sketches | Song-Hai Zhang, Yuan-Chen Guo, Qing-Wen Gu | We investigate the problem of generating 3D meshes from single free-hand sketches, aiming at fast 3D modeling for novice users. It can be regarded as a single-view reconstruction problem, but with unique challenges, brought by the variation and conciseness of sketches. Ambiguities in poorly-drawn sketches could make it hard to determine how the sketched object is posed. In this paper, we address the importance of viewpoint specification for overcoming such ambiguities, and propose a novel view-aware generation approach. By explicitly conditioning the generation process on a given viewpoint, our method can generate plausible shapes automatically with predicted viewpoints, or with specified viewpoints to help users better express their intentions. Extensive evaluations on various datasets demonstrate the effectiveness of our view-aware design in solving sketch ambiguities and improving reconstruction quality. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Sketch2Model_View-Aware_3D_Modeling_From_Single_Free-Hand_Sketches_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.06663 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Sketch2Model_View-Aware_3D_Modeling_From_Single_Free-Hand_Sketches_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Sketch2Model_View-Aware_3D_Modeling_From_Single_Free-Hand_Sketches_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Sketch2Model_View-Aware_3D_CVPR_2021_supplemental.pdf | null |
CASTing Your Model: Learning To Localize Improves Self-Supervised Representations | Ramprasaath R. Selvaraju, Karan Desai, Justin Johnson, Nikhil Naik | Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. Despite their success these methods have been primarily applied to unlabeled ImageNet images, and show marginal gains when trained on larger sets of uncurated images. We hypothesize that current SSL methods perform best on iconic images, and struggle on complex scene images with many objects. Analyzing contrastive SSL methods shows that they have poor visual grounding and receive poor supervisory signal when trained on scene images. We propose Contrast Attention-Supervised Tuning (CAST) to overcome these limitations. CAST uses unsupervised saliency maps to intelligently sample crops, and to provide grounding supervision via a Grad-CAM attention loss. Experiments on COCO show that CAST significantly improves the features learned by SSL methods on scene images, and further experiments show that CAST-trained models are more robust to changes in backgrounds. We hope that CAST can improve the ability of SSL methods to learn from complex non-iconic images. Our code is available at https://github.com/salesforce/CAST. | https://openaccess.thecvf.com/content/CVPR2021/papers/Selvaraju_CASTing_Your_Model_Learning_To_Localize_Improves_Self-Supervised_Representations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04630 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Selvaraju_CASTing_Your_Model_Learning_To_Localize_Improves_Self-Supervised_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Selvaraju_CASTing_Your_Model_Learning_To_Localize_Improves_Self-Supervised_Representations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Selvaraju_CASTing_Your_Model_CVPR_2021_supplemental.pdf | null |
Robust Consistent Video Depth Estimation | Johannes Kopf, Xuejian Rong, Jia-Bin Huang | We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video. We integrate a learning-based depth prior, in the form of a convolutional neural network trained for single-image depth estimation, with geometric optimization, to estimate a smooth camera trajectory as well as detailed and stable depth reconstruction. Our algorithm combines two complementary techniques: (1) flexible deformation-splines for low-frequency large-scale alignment and (2) geometry-aware depth filtering for high-frequency alignment of fine depth details. In contrast to prior approaches, our method does not require camera poses as input and achieves robust reconstruction for challenging hand-held cell phone captures that contain a significant amount of noise, shake, motion blur, and rolling shutter deformations. Our method quantitatively outperforms state-of-the-arts on the Sintel benchmark for both depth and pose estimations, and attains favorable qualitative results across diverse wild datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kopf_Robust_Consistent_Video_Depth_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.05901 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kopf_Robust_Consistent_Video_Depth_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kopf_Robust_Consistent_Video_Depth_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kopf_Robust_Consistent_Video_CVPR_2021_supplemental.pdf | null |
LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents | ByeoungDo Kim, Seong Hyeon Park, Seokhwan Lee, Elbek Khoshimjonov, Dongsuk Kum, Junsoo Kim, Jeong Soo Kim, Jun Won Choi | In this paper, we address the problem of predicting the future motion of a dynamic agent (called a target agent) given its current and past states as well as the information on its environment. It is paramount to develop a prediction model that can exploit the contextual information in both static and dynamic environments surrounding the target agent and generate diverse trajectory samples that are meaningful in a traffic context. We propose a novel prediction model, referred to as the lane-aware prediction (LaPred) network, which uses the instance-level lane entities extracted from a semantic map to predict the multimodal future trajectories. For each lane candidate found in the neighborhood of the target agent, LaPred extracts the joint features relating the lane and the trajectories of the neighboring agents. Then, the features for all lane candidates are fused with the attention weights learned through a self-supervised learning task that identifies the lane candidate likely to be followed by the target agent. Using the instance-level lane information, LaPred can produce the trajectories compliant with the surroundings better than 2D raster image-based methods and generate the diverse future trajectories given multiple lane candidates. The experiments conducted on the public nuScenes dataset and Argoverse dataset demonstrate that the proposed LaPred method significantly outperforms the existing prediction models, achieving state-of-the-art performance in the benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_LaPred_Lane-Aware_Prediction_of_Multi-Modal_Future_Trajectories_of_Dynamic_Agents_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00249 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_LaPred_Lane-Aware_Prediction_of_Multi-Modal_Future_Trajectories_of_Dynamic_Agents_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_LaPred_Lane-Aware_Prediction_of_Multi-Modal_Future_Trajectories_of_Dynamic_Agents_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_LaPred_Lane-Aware_Prediction_CVPR_2021_supplemental.pdf | null |
NeuralRecon: Real-Time Coherent 3D Reconstruction From Monocular Video | Jiaming Sun, Yiming Xie, Linghao Chen, Xiaowei Zhou, Hujun Bao | We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we propose to directly reconstruct local surfaces represented as sparse TSDF volumes for each video fragment sequentially by a neural network. A learning-based TSDF fusion module based on gated recurrent units is used to guide the network to fuse features from previous fragments. This design allows the network to capture local smoothness prior and global shape prior of 3D surfaces when sequentially reconstructing the surfaces, resulting in accurate, coherent, and real-time surface reconstruction. The experiments on ScanNet and 7-Scenes datasets show that our system outperforms state-of-the-art methods in terms of both accuracy and speed. To the best of our knowledge, this is the first learning-based system that is able to reconstruct dense coherent 3D geometry in real-time. Code is available at the project page: https://zju3dv.github.io/neuralrecon/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_NeuralRecon_Real-Time_Coherent_3D_Reconstruction_From_Monocular_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00681 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_NeuralRecon_Real-Time_Coherent_3D_Reconstruction_From_Monocular_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_NeuralRecon_Real-Time_Coherent_3D_Reconstruction_From_Monocular_Video_CVPR_2021_paper.html | CVPR 2021 | null | null |
Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation | Hang Zhou, Yasheng Sun, Wayne Wu, Chen Change Loy, Xiaogang Wang, Ziwei Liu | While accurate lip synchronization has been achieved for arbitrary-subject audio-driven talking face generation, the problem of how to efficiently drive the head pose remains. Previous methods rely on pre-estimated structural information such as landmarks and 3D parameters, aiming to generate personalized rhythmic movements. However, the inaccuracy of such estimated information under extreme conditions would lead to degradation problems. In this paper, we propose a clean yet effective framework to generate pose-controllable talking faces. We operate on non-aligned raw face images, using only a single photo as an identity reference. The key is to modularize audio-visual representations by devising an implicit low-dimension pose code. Substantially, both speech content and head pose information lie in a joint non-identity embedding space. While speech content information can be defined by learning the intrinsic synchronization between audio-visual modalities, we identify that a pose code will be complementarily learned in a modulated convolution-based reconstruction framework. Extensive experiments show that our method generates accurately lip-synced talking faces whose poses are controllable by other videos. Moreover, our model has multiple advanced capabilities including extreme view robustness and talking face frontalization. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Pose-Controllable_Talking_Face_Generation_by_Implicitly_Modularized_Audio-Visual_Representation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11116 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Pose-Controllable_Talking_Face_Generation_by_Implicitly_Modularized_Audio-Visual_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Pose-Controllable_Talking_Face_Generation_by_Implicitly_Modularized_Audio-Visual_Representation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Pose-Controllable_Talking_Face_CVPR_2021_supplemental.zip | null |
Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion | Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang | We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation module using a novel top-k filtering strategy in reading the space-time memory. To effectively take the user's intent into account, a novel difference-aware module is proposed to learn how to properly fuse the masks before and after each interaction, which are aligned with the target frames by employing the space-time memory. We evaluate our method both qualitatively and quantitatively with different forms of user interactions (e.g., scribbles, clicks) on DAVIS to show that our method outperforms current state-of-the-art algorithms while requiring fewer frame interactions, with the additional advantage in generalizing to different types of user interactions. We contribute a large-scale synthetic VOS dataset with pixel-accurate segmentation of 4.8M frames to accompany our source codes to facilitate future research. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07941 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Modular_Interactive_Video_CVPR_2021_supplemental.pdf | null |
A Sliced Wasserstein Loss for Neural Texture Synthesis | Eric Heitz, Kenneth Vanhoey, Thomas Chambon, Laurent Belcour | We address the problem of computing a textural loss based on the statistics extracted from the feature activations of a convolutional neural network optimized for object recognition (e.g. VGG-19). The underlying mathematical problem is the measure of the distance between two distributions in feature space. The Gram-matrix loss is the ubiquitous approximation for this problem but it is subject to several shortcomings. Our goal is to promote the Sliced Wasserstein Distance as a replacement for it. It is theoretically proven, practical, simple to implement, and achieves results that are visually superior for texture synthesis by optimization or training generative neural networks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Heitz_A_Sliced_Wasserstein_Loss_for_Neural_Texture_Synthesis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.07229 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Heitz_A_Sliced_Wasserstein_Loss_for_Neural_Texture_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Heitz_A_Sliced_Wasserstein_Loss_for_Neural_Texture_Synthesis_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Accurate Dense Correspondences and When To Trust Them | Prune Truong, Martin Danelljan, Luc Van Gool, Radu Timofte | Establishing dense correspondences between a pair of images is an important and general problem. However, dense flow estimation is often inaccurate in the case of large displacements or homogeneous regions. For most applications and down-stream tasks, such as pose estimation, image manipulation, or 3D reconstruction, it is crucial to know when and where to trust the estimated matches. In this work, we aim to estimate a dense flow field relating two images, coupled with a robust pixel-wise confidence map indicating the reliability and accuracy of the prediction. We develop a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty. In particular, we parametrize the predictive distribution as a constrained mixture model, ensuring better modelling of both accurate flow predictions and outliers. Moreover, we develop an architecture and training strategy tailored for robust and generalizable uncertainty prediction in the context of self-supervised training. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets. We further validate the usefulness of our probabilistic confidence estimation for the task of pose estimation. Code and models are available at https://github.com/PruneTruong/PDCNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Truong_Learning_Accurate_Dense_Correspondences_and_When_To_Trust_Them_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.01710 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Truong_Learning_Accurate_Dense_Correspondences_and_When_To_Trust_Them_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Truong_Learning_Accurate_Dense_Correspondences_and_When_To_Trust_Them_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Truong_Learning_Accurate_Dense_CVPR_2021_supplemental.pdf | null |
Learning Better Visual Dialog Agents With Pretrained Visual-Linguistic Representation | Tao Tu, Qing Ping, Govindarajan Thattai, Gokhan Tur, Prem Natarajan | GuessWhat?! is a visual dialog guessing game which incorporates a Questioner agent that generates a sequence of questions, while an Oracle agent answers the respective questions about a target object in an image. Based on this dialog history between the Questioner and the Oracle, a Guesser agent makes a final guess of the target object. While previous work has focused on dialogue policy optimization and visual-linguistic information fusion, most work learns the vision-linguistic encoding for the three agents solely on the GuessWhat?! dataset without shared and prior knowledge of vision-linguistic representation. To bridge these gaps, this paper proposes new Oracle, Guesser and Questioner models that take advantage of a pretrained vision-linguistic model, VilBert. For Oracle model, we introduce a two-way background/target fusion mechanism to understand both intra and inter-object questions. For Guesser model, we introduce a state-estimator that best utilizes Vilbert's strength in single-turn referring expression comprehension. For the Questioner, we share the state-estimator from pretrained Guesser with Questioner to guide the question generator. Experimental results show that our proposed models outperform state-of-the-art models significantly by 7%, 10%, 12% for Oracle, Guesser and End-to-End Questioner respectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tu_Learning_Better_Visual_Dialog_Agents_With_Pretrained_Visual-Linguistic_Representation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.11541 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tu_Learning_Better_Visual_Dialog_Agents_With_Pretrained_Visual-Linguistic_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tu_Learning_Better_Visual_Dialog_Agents_With_Pretrained_Visual-Linguistic_Representation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tu_Learning_Better_Visual_CVPR_2021_supplemental.pdf | null |
Restoring Extremely Dark Images in Real Time | Mohit Lamba, Kaushik Mitra | A practical low-light enhancement solution must be computationally fast, memory-efficient, and achieve a visually appealing restoration. Most of the existing methods target restoration quality and thus compromise on speed and memory requirements, raising concerns about their real-world deployability. We propose a new deep learning architecture for extreme low-light single image restoration, which is exceptionally lightweight, remarkably fast, and produces a restoration that is perceptually at par with state-of-the-art computationally intense models. To achieve this, we do most of the processing in the higher scale-spaces, skipping the intermediate-scales wherever possible. Also unique to our model is the potential to process all the scale-spaces concurrently, offering an additional 30% speedup without compromising the restoration quality. Pre-amplification of the dark raw-image is an important step in extreme low-light image enhancement. Most of the existing state-of-the-art methods need GT exposure value to estimate the pre-amplification factor, which is not practically feasible. Thus, we propose an amplifier module that estimates the amplification factor using only the input raw image and can be used "off-the-shelf"" with pre-trained models without any fine-tuning. We show that our model can restore an ultra-high-definition 4K resolution image in just 1sec on a CPU and at 32fps on a GPU and yet maintain a competitive restoration quality. We also show that our proposed model, without any fine-tuning, generalizes well to cameras not seen during training and to subsequent tasks such as object detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lamba_Restoring_Extremely_Dark_Images_in_Real_Time_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lamba_Restoring_Extremely_Dark_Images_in_Real_Time_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lamba_Restoring_Extremely_Dark_Images_in_Real_Time_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lamba_Restoring_Extremely_Dark_CVPR_2021_supplemental.pdf | null |
Weakly-Supervised Instance Segmentation via Class-Agnostic Learning With Salient Images | Xinggang Wang, Jiapei Feng, Bin Hu, Qi Ding, Longjin Ran, Xiaoxin Chen, Wenyu Liu | Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based solution for weakly-supervised instance segmentation. The BoxCaseg model is jointly trained using box-supervised images and salient images in a multi-task learning manner. The fine-annotated salient images provide class-agnostic and precise object localization guidance for box-supervised images. The object masks predicted by a pretrained BoxCaseg model are refined via a novel merged and dropped strategy as proxy ground truth to train a Mask R-CNN for weakly-supervised instance segmentation. Only using 7991 salient images, the weakly-supervised Mask R-CNN is on par with fully-supervised Mask R-CNN on PASCAL VOC and significantly outperforms previous state-of-the-art box-supervised instance segmentation methods on COCO. The source code, pretrained models and datasets are available at https://github.com/hustvl/BoxCaseg. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Weakly-Supervised_Instance_Segmentation_via_Class-Agnostic_Learning_With_Salient_Images_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01526 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Weakly-Supervised_Instance_Segmentation_via_Class-Agnostic_Learning_With_Salient_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Weakly-Supervised_Instance_Segmentation_via_Class-Agnostic_Learning_With_Salient_Images_CVPR_2021_paper.html | CVPR 2021 | null | null |
Spoken Moments: Learning Joint Audio-Visual Representations From Video Descriptions | Mathew Monfort, SouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, Aude Oliva | When people observe events, they are able to abstract key information and build concise summaries of what is happening. These summaries include contextual and semantic information describing the important high-level details (what, where, who and how) of the observed event and exclude background information that is deemed unimportant to the observer. With this in mind, the descriptions people generate for videos of different dynamic events can greatly improve our understanding of the key information of interest in each video. These descriptions can be captured in captions that provide expanded attributes for video labeling (e.g. actions/objects/scenes/sentiment/etc.) while allowing us to gain new insight into what people find important or necessary to summarize specific events. Existing caption datasets for video understanding are either small in scale or restricted to a specific domain. To address this, we present the Spoken Moments (S-MiT) dataset of 500k spoken captions each attributed to a unique short video depicting a broad range of different events. We collect our descriptions using audio recordings to ensure that they remain as natural and concise as possible while allowing us to scale the size of a large classification dataset. In order to utilize our proposed dataset, we present a novel Adaptive Mean Margin (AMM) approach to contrastive learning and evaluate our models on video/caption retrieval on multiple datasets. We show that our AMM approach consistently improves our results and that models trained on our Spoken Moments dataset generalize better than those trained on other video-caption datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Monfort_Spoken_Moments_Learning_Joint_Audio-Visual_Representations_From_Video_Descriptions_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.04489 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Monfort_Spoken_Moments_Learning_Joint_Audio-Visual_Representations_From_Video_Descriptions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Monfort_Spoken_Moments_Learning_Joint_Audio-Visual_Representations_From_Video_Descriptions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Monfort_Spoken_Moments_Learning_CVPR_2021_supplemental.pdf | null |
Image Restoration for Under-Display Camera | Yuqian Zhou, David Ren, Neil Emerton, Sehoon Lim, Timothy Large | The new trend of full-screen devices encourages us to position a camera behind a screen. Removing the bezel and centralizing the camera under the screen brings larger display-to-body ratio and enhances eye contact in video chat, but also causes image degradation. In this paper, we focus on a newly-defined Under-Display Camera (UDC), as a novel real-world single image restoration problem. First, we take a 4k Transparent OLED (T-OLED) and a phone Pentile OLED (P-OLED) and analyze their optical systems to understand the degradation. Second, we design a Monitor-Camera Imaging System (MCIS) for easier real pair data acquisition, and a model-based data synthesizing pipeline to generate Point Spread Function (PSF) and UDC data only from display pattern and camera measurements. Finally, we resolve the complicated degradation using deconvolution-based pipeline and learning-based methods. Our model demonstrates a real-time high-quality restoration. The presented methods and results reveal the promising research values and directions of UDC. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Image_Restoration_for_Under-Display_Camera_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.04857 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Image_Restoration_for_Under-Display_Camera_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Image_Restoration_for_Under-Display_Camera_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Image_Restoration_for_CVPR_2021_supplemental.pdf | null |
Unbiased Mean Teacher for Cross-Domain Object Detection | Jinhong Deng, Wen Li, Yuhua Chen, Lixin Duan | Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distinctive domains. In this paper, we propose a new Unbiased Mean Teacher (UMT) model for cross-domain object detection. We reveal that there often exists a considerable model bias for the simple mean teacher (MT) model in cross-domain scenarios, and eliminate the model bias with several simple yet highly effective strategies. In particular, for the teacher model, we propose a cross-domain distillation for MT to maximally exploit the expertise of the teacher model. Second, for the student model, we also alleviate its bias by augmenting training samples with pixel-level adaptation. Finally, for the teaching process, we employ an out-of-distribution estimation strategy to select samples that most fit the current model to further enhance the cross-domain distillation process. By tackling the model bias issue with these strategies, our UMT model achieves mAPs of 44.1%, 58.1%, 41.7%, and 43.1% on benchmark datasets Clipart1k, Watercolor2k, Foggy Cityscapes, and Cityscapes, respectively, which outperforms the existing state-of-the-art results in notable margins. Our implementation is available at https://github.com/kinredon/umt. | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.00707 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
How2Sign: A Large-Scale Multimodal Dataset for Continuous American Sign Language | Amanda Duarte, Shruti Palaskar, Lucas Ventura, Deepti Ghadiyaram, Kenneth DeHaan, Florian Metze, Jordi Torres, Xavier Giro-i-Nieto | One of the factors that have hindered progress in the areas of sign language recognition, translation, and production is the absence of large annotated datasets. Towards this end, we introduce How2Sign, a multimodal and multiview continuous American Sign Language (ASL) dataset, consisting of a parallel corpus of more than 80 hours of sign language videos and a set of corresponding modalities including speech, English transcripts, and depth. A three-hour subset was further recorded in the Panoptic studio enabling detailed 3D pose estimation. To evaluate the potential of How2Sign for real-world impact, we conduct a study with ASL signers and show that synthesized videos using our dataset can indeed be understood. The study further gives insights on challenges that computer vision should address in order to make progress in this field. Dataset website: http://how2sign.github.io/ | https://openaccess.thecvf.com/content/CVPR2021/papers/Duarte_How2Sign_A_Large-Scale_Multimodal_Dataset_for_Continuous_American_Sign_Language_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.08143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Duarte_How2Sign_A_Large-Scale_Multimodal_Dataset_for_Continuous_American_Sign_Language_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Duarte_How2Sign_A_Large-Scale_Multimodal_Dataset_for_Continuous_American_Sign_Language_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duarte_How2Sign_A_Large-Scale_CVPR_2021_supplemental.pdf | null |
Indoor Lighting Estimation Using an Event Camera | Zehao Chen, Qian Zheng, Peisong Niu, Huajin Tang, Gang Pan | Image-based methods for indoor lighting estimation suffer from the problem of intensity-distance ambiguity. This paper introduces a novel setup to help alleviate the ambiguity based on the event camera. We further demonstrate that estimating the distance of a light source becomes a well-posed problem under this setup, based on which an optimization-based method and a learning-based method are proposed. Our experimental results validate that our approaches not only achieve superior performance for indoor lighting estimation (especially for the close light) but also significantly alleviate the intensity-distance ambiguity. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Indoor_Lighting_Estimation_Using_an_Event_Camera_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Indoor_Lighting_Estimation_Using_an_Event_Camera_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Indoor_Lighting_Estimation_Using_an_Event_Camera_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Indoor_Lighting_Estimation_CVPR_2021_supplemental.pdf | null |
Shot Contrastive Self-Supervised Learning for Scene Boundary Detection | Shixing Chen, Xiaohan Nie, David Fan, Dongqing Zhang, Vimal Bhat, Raffay Hamid | Scenes play a crucial role in breaking the storyline of movies and TV episodes into semantically cohesive parts. However, given their complex temporal structure, finding scene boundaries can be a challenging task requiring large amounts of labeled training data. To address this challenge, we present a self-supervised shot contrastive learning approach (ShotCoL) to learn a shot representation that maximizes the similarity between nearby shots compared to randomly selected shots. We show how to apply our learned shot representation for the task of scene boundary detection to offer state-of-the-art performance on the MovieNet dataset while requiring only 25% of the training labels, using 9x fewer model parameters and offering 7x faster runtime. To assess the effectiveness of ShotCoL on novel applications of scene boundary detection, we take on the problem of finding timestamps in movies and TV episodes where video-ads can be inserted while offering a minimally disruptive viewing experience. To this end, we collected a new dataset called AdCuepoints with 3,975 movies and TV episodes, 2.2 million shots and 19,119 minimally disruptive ad cue-point labels. We present a thorough empirical analysis on this dataset demonstrating the effectiveness of ShotCoL for ad cue-points detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Shot_Contrastive_Self-Supervised_Learning_for_Scene_Boundary_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.13537 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Shot_Contrastive_Self-Supervised_Learning_for_Scene_Boundary_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Shot_Contrastive_Self-Supervised_Learning_for_Scene_Boundary_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Shot_Contrastive_Self-Supervised_CVPR_2021_supplemental.pdf | null |
Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark | Joakim Bruslund Haurum, Thomas B. Moeslund | Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of this process is therefore of high interest. So far, the success of computer vision approaches for sewer defect classification has been limited when compared to the success in other fields mainly due to the lack of public datasets. To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML. The Sewer-ML dataset consists of 1.3 million images annotated by professional sewer inspectors from three different utility companies across nine years. Together with the dataset, we also present a benchmark algorithm and a novel metric for assessing performance. The benchmark algorithm is a result of evaluating 12 state-of-the-art algorithms, six from the sewer defect classification domain and six from the multi-label classification domain, and combining the best performing algorithms. The novel metric is a class-importance weighted F2 score, F2-CIW, reflecting the economic impact of each class, used together with the normal pipe F1 score, F1-Normal. The benchmark algorithm achieves an F2-CIW score of 55.11% and F1-Normal score of 90.94%, leaving ample room for improvement on the Sewer-ML dataset. The code, models, and dataset are available at the project page http://vap.aau.dk/sewer-ml | https://openaccess.thecvf.com/content/CVPR2021/papers/Haurum_Sewer-ML_A_Multi-Label_Sewer_Defect_Classification_Dataset_and_Benchmark_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Haurum_Sewer-ML_A_Multi-Label_Sewer_Defect_Classification_Dataset_and_Benchmark_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Haurum_Sewer-ML_A_Multi-Label_Sewer_Defect_Classification_Dataset_and_Benchmark_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Haurum_Sewer-ML_A_Multi-Label_CVPR_2021_supplemental.pdf | null |
Joint-DetNAS: Upgrade Your Detector With NAS, Pruning and Dynamic Distillation | Lewei Yao, Renjie Pi, Hang Xu, Wei Zhang, Zhenguo Li, Tong Zhang | We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead of naively pipelining these techniques, our Joint-DetNAS optimizes them jointly. The algorithm consists of two core processes: student morphism optimizes the student's architecture and removes the redundant parameters, while dynamic distillation aims to find the optimal matching teacher. For student morphism, weight inheritance strategy is adopted, allowing the student to flexibly update its architecture while fully utilize the predecessor's weights, which considerably accelerates the search; To facilitate dynamic distillation, an elastic teacher pool is trained via integrated progressive shrinking strategy, from which teacher detectors can be sampled without additional cost in subsequent searches. Given a base detector as the input, our algorithm directly outputs the derived student detector with high performance without additional training. Experiments demonstrate that our Joint-DetNAS outperforms the naive pipelining approach by a great margin. Given a classic R101-FPN as the base detector, Joint-DetNAS is able to boost its mAP from 41.4 to 43.9 on MS COCO and reduce the latency by 47%, which is on par with the SOTA EfficientDet while requiring less search cost. We hope our proposed method can provide the community with a new way of jointly optimizing NAS, KD and pruning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yao_Joint-DetNAS_Upgrade_Your_Detector_With_NAS_Pruning_and_Dynamic_Distillation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Joint-DetNAS_Upgrade_Your_Detector_With_NAS_Pruning_and_Dynamic_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Joint-DetNAS_Upgrade_Your_Detector_With_NAS_Pruning_and_Dynamic_Distillation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yao_Joint-DetNAS_Upgrade_Your_CVPR_2021_supplemental.pdf | null |
Back-Tracing Representative Points for Voting-Based 3D Object Detection in Point Clouds | Bowen Cheng, Lu Sheng, Shaoshuai Shi, Ming Yang, Dong Xu | 3D object detection in point clouds is a challenging vision task that benefits various applications for understanding the 3D visual world. Lots of recent research focuses on how to exploit end-to-end trainable Hough voting for generating object proposals. However, the current voting strategy can only receive partial votes from the surfaces of potential objects together with severe outlier votes from the cluttered backgrounds, which hampers full utilization of the information from the input point clouds. Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds. Therefore, this bottom-up and then top-down strategy in our BRNet enforces mutual consistency between the predicted vote centers and the raw surface points and thus achieves more reliable and flexible object localization and class prediction results. Our BRNet is simple but effective, which significantly outperforms the state-of-the-art methods on two large-scale point cloud datasets, ScanNet V2 (+7.5% in terms of [email protected]) and SUN RGB-D (+4.7% in terms of [email protected]), while it is still lightweight and efficient. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Back-Tracing_Representative_Points_for_Voting-Based_3D_Object_Detection_in_Point_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06114 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Back-Tracing_Representative_Points_for_Voting-Based_3D_Object_Detection_in_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Back-Tracing_Representative_Points_for_Voting-Based_3D_Object_Detection_in_Point_CVPR_2021_paper.html | CVPR 2021 | null | null |
High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network | Jie Liang, Hui Zeng, Lei Zhang | Existing image-to-image translation (I2IT) methods are either constrained to low-resolution images or long inference time due to their heavy computational burden on the convolution of high-resolution feature maps. In this paper, we focus on speeding-up the high-resolution photorealistic I2IT tasks based on closed-form Laplacian pyramid decomposition and reconstruction. Specifically, we reveal that the attribute transformations, such as illumination and color manipulation, relate more to the low-frequency component, while the content details can be adaptively refined on high-frequency components. We consequently propose a Laplacian Pyramid Translation Network (LPTN) to simultaneously perform these two tasks, where we design a lightweight network for translating the low-frequency component with reduced resolution and a progressive masking strategy to efficiently refine the high-frequency ones. Our model avoids most of the heavy computation consumed by processing high-resolution feature maps and faithfully preserves the image details. Extensive experimental results on various tasks demonstrate that the proposed method can translate 4K images in real-time using one normal GPU while achieving comparable transformation performance against existing methods. Datasets and codes are available: https://github.com/csjliang/LPTN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_High-Resolution_Photorealistic_Image_Translation_in_Real-Time_A_Laplacian_Pyramid_Translation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_High-Resolution_Photorealistic_Image_Translation_in_Real-Time_A_Laplacian_Pyramid_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_High-Resolution_Photorealistic_Image_Translation_in_Real-Time_A_Laplacian_Pyramid_Translation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liang_High-Resolution_Photorealistic_Image_CVPR_2021_supplemental.pdf | null |
End-to-End Video Instance Segmentation With Transformers | Yuqing Wang, Zhaoliang Xu, Xinlong Wang, Chunhua Shen, Baoshan Cheng, Hao Shen, Huaxia Xia | Video instance segmentation (VIS) is the task that requires simultaneously classifying, segmenting and tracking object instances of interest in video. Recent methods typically develop sophisticated pipelines to tackle this task. Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem. Given a video clip consisting of multiple image frames as input, VisTR outputs the sequence of masks for each instance in the video in order directly. At the core is a new, effective instance sequence matching and segmentation strategy, which supervises and segments instances at the sequence level as a whole. VisTR frames the instance segmentation and tracking in the same perspective of similarity learning, thus considerably simplifying the overall pipeline and is significantly different from existing approaches. Without bells and whistles, VisTR achieves the highest speed among all existing VIS models, and achieves the best result among methods using single model on the YouTube-VIS dataset. For the first time, we demonstrate a much simpler and faster video instance segmentation framework achieving competitive accuracy. We hope that VisTR can motivate future research for more video understanding tasks. Code is available at: https://git.io/VisTR | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_End-to-End_Video_Instance_Segmentation_With_Transformers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14503 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_End-to-End_Video_Instance_Segmentation_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_End-to-End_Video_Instance_Segmentation_With_Transformers_CVPR_2021_paper.html | CVPR 2021 | null | null |
VoxelContext-Net: An Octree Based Framework for Point Cloud Compression | Zizheng Que, Guo Lu, Dong Xu | In this paper, we propose a two-stage deep learning framework called VoxelContext-Net for both static and dynamic point cloud compression. Taking advantages of both octree based methods and voxel based schemes, our approach employs the voxel context to compress the octree structured data. Specifically, we first extract the local voxel representation that encodes the spatial neighbouring context information for each node in the constructed octree. Then, in the entropy coding stage, we propose a voxel context based deep entropy model to compress the symbols of non-leaf nodes in a lossless way. Furthermore, for dynamic point cloud compression, we additionally introduce the local voxel representations from the temporal neighbouring point clouds to exploit temporal dependency. More importantly, to alleviate the distortion from the octree construction procedure, we propose a voxel context based 3D coordinate refinement method to produce more accurate reconstructed point cloud at the decoder side, which is applicable to both static and dynamic point cloud compression. The comprehensive experiments on both static and dynamic point cloud benchmark datasets(e.g., ScanNet and Semantic KITTI) clearly demonstrate the effectiveness of our newly proposed method VoxelContext-Net for 3D point cloud geometry compression. | https://openaccess.thecvf.com/content/CVPR2021/papers/Que_VoxelContext-Net_An_Octree_Based_Framework_for_Point_Cloud_Compression_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Que_VoxelContext-Net_An_Octree_Based_Framework_for_Point_Cloud_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Que_VoxelContext-Net_An_Octree_Based_Framework_for_Point_Cloud_Compression_CVPR_2021_paper.html | CVPR 2021 | null | null |
A Second-Order Approach to Learning With Instance-Dependent Label Noise | Zhaowei Zhu, Tongliang Liu, Yang Liu | The presence of label noise often misleads the training of deep neural networks. Departing from the recent literature which largely assumes the label noise rate is only determined by the true label class, the errors in human-annotated labels are more likely to be dependent on the difficulty levels of tasks, resulting in settings with instance-dependent label noise. We first provide evidences that the heterogeneous instance-dependent label noise is effectively down-weighting the examples with higher noise rates in a non-uniform way and thus causes imbalances, rendering the strategy of directly applying methods for class-dependent label noise questionable. Built on a recent work peer loss [24], we then propose and study the potentials of a second-order approach that leverages the estimation of several covariance terms defined between the instance-dependent noise rates and the Bayes optimal label. We show that this set of second-order statistics successfully captures the induced imbalances. We further proceed to show that with the help of the estimated second-order statistics, we identify a new loss function whose expected risk of a classifier under instance-dependent label noise is equivalent to a new problem with only class-dependent label noise. This fact allows us to apply existing solutions to handle this better-studied setting. We provide an efficient procedure to estimate these second-order statistics without accessing either ground truth labels or prior knowledge of the noise rates. Experiments on CIFAR10 and CIFAR100 with synthetic instance-dependent label noise and Clothing1M with real-world human label noise verify our approach. Our implementation is available at https://github.com/UCSC-REAL/CAL. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_A_Second-Order_Approach_to_Learning_With_Instance-Dependent_Label_Noise_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11854 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_A_Second-Order_Approach_to_Learning_With_Instance-Dependent_Label_Noise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_A_Second-Order_Approach_to_Learning_With_Instance-Dependent_Label_Noise_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_A_Second-Order_Approach_CVPR_2021_supplemental.pdf | null |
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration | Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, Yulan Guo | Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ao_SpinNet_Learning_a_General_Surface_Descriptor_for_3D_Point_Cloud_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ao_SpinNet_Learning_a_General_Surface_Descriptor_for_3D_Point_Cloud_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ao_SpinNet_Learning_a_General_Surface_Descriptor_for_3D_Point_Cloud_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ao_SpinNet_Learning_a_CVPR_2021_supplemental.pdf | null |
FSDR: Frequency Space Domain Randomization for Domain Generalization | Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu | Domain generalization aims to learn a generalizable model from a `known' source domain for various `unknown' target domains. It has been studied widely by domain randomization that transfers source images to different styles in spatial space for learning domain-agnostic features. However, most existing randomization methods use GANs that often lack of controls and even alter semantic structures of images undesirably. Inspired by the idea of JPEG that converts spatial images into multiple frequency components (FCs), we propose Frequency Space Domain Randomization (FSDR) that randomizes images in frequency space by keeping domain-invariant FCs (DIFs) and randomizing domain-variant FCs (DVFs) only. FSDR has two unique features: 1) it decomposes images into DIFs and DVFs which allows explicit access and manipulation of them and more controllable randomization; 2) it has minimal effects on semantic structures of images and domain-invariant features. We examined domain variance and invariance property of FCs statistically and designed a network that can identify and fuse DIFs and DVFs dynamically through iterative learning. Extensive experiments over multiple domain generalizable segmentation tasks show that FSDR achieves superior segmentation and its performance is even on par with domain adaptation methods that access target data in training. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_FSDR_Frequency_Space_Domain_Randomization_for_Domain_Generalization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02370 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_FSDR_Frequency_Space_Domain_Randomization_for_Domain_Generalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_FSDR_Frequency_Space_Domain_Randomization_for_Domain_Generalization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_FSDR_Frequency_Space_CVPR_2021_supplemental.pdf | null |
DualAST: Dual Style-Learning Networks for Artistic Style Transfer | Haibo Chen, Lei Zhao, Zhizhong Wang, Huiming Zhang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu | Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles. Existing methods learn styles from either a single style example or a collection of artworks. Accordingly, the stylization results are either inferior in visual quality or limited in style controllability. To tackle this problem, we propose a novel Dual Style-Learning Artistic Style Transfer (DualAST) framework to learn simultaneously both the holistic artist-style (from a collection of artworks) and the specific artwork-style (from a single style image): the artist-style sets the tone (i.e., the overall feeling) for the stylized image, while the artwork-style determines the details of the stylized image, such as color and texture. Moreover, we introduce a Style-Control Block (SCB) to adjust the styles of generated images with a set of learnable style-control factors. We conduct extensive experiments to evaluate the performance of the proposed framework, the results of which confirm the superiority of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_DualAST_Dual_Style-Learning_Networks_for_Artistic_Style_Transfer_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_DualAST_Dual_Style-Learning_Networks_for_Artistic_Style_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_DualAST_Dual_Style-Learning_Networks_for_Artistic_Style_Transfer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_DualAST_Dual_Style-Learning_CVPR_2021_supplemental.pdf | null |
Learning a Proposal Classifier for Multiple Object Tracking | Peng Dai, Renliang Weng, Wongun Choi, Changshui Zhang, Zhangping He, Wei Ding | The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-association problem in an end-to-end fashion. In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph. This framework is similar to the two-stage object detector Faster RCNN, and can solve the MOT problem in a data-driven way. For proposal generation, we propose an iterative graph clustering method to reduce the computational cost while maintaining the quality of the generated proposals. For proposal scoring, we deploy a trainable graph-convolutional-network (GCN) to learn the structural patterns of the generated proposals and rank them according to the estimated quality scores. For trajectory inference, a simple deoverlapping strategy is adopted to generate tracking output while complying with the constraints that no detection can be assigned to more than one track. We experimentally demonstrate that the proposed method achieves a clear performance improvement in both MOTA and IDF1 with respect to previous state-of-the-art on two public benchmarks. Our code is available at https://github.com/daip13/LPC_MOT.git. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Learning_a_Proposal_Classifier_for_Multiple_Object_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07889 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_Learning_a_Proposal_Classifier_for_Multiple_Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_Learning_a_Proposal_Classifier_for_Multiple_Object_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_Learning_a_Proposal_CVPR_2021_supplemental.pdf | null |
Multi-Attentional Deepfake Detection | Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, Nenghai Yu | Face forgery by deepfake is widely spread over the internet and has raised severe societal concerns. Recently, how to detect such forgery contents has become a hot research topic and many deepfake detection methods have been proposed. Most of them model deepfake detection as a vanilla binary classification problem, i.e, first use a backbone network to extract a global feature and then feed it into a binary classifier (real/fake). But since the difference between the real and fake images in this task is often subtle and local, we argue this vanilla solution is not optimal. In this paper, we instead formulate deepfake detection as a fine-grained classification problem and propose a new multi-attentional deepfake detection network. Specifically, it consists of three key components: 1) multiple spatial attention heads to make the network attend to different local parts; 2) textural feature enhancement block to zoom in the subtle artifacts in shallow features; 3) aggregate the low-level textural feature and high-level semantic features guided by the attention maps. Moreover, to address the learning difficulty of this network, we further introduce a new regional independence loss and an attention guided data augmentation strategy. Through extensive experiments on different datasets, we demonstrate the superiority of our method over the vanilla binary classifier counterparts, and achieve state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Multi-Attentional_Deepfake_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02406 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Multi-Attentional_Deepfake_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Multi-Attentional_Deepfake_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
SOLD2: Self-Supervised Occlusion-Aware Line Description and Detection | Remi Pautrat, Juan-Ting Lin, Viktor Larsson, Martin R. Oswald, Marc Pollefeys | Compared to feature point detection and description, detecting and matching line segments offer additional challenges. Yet, line features represent a promising complement to points for multi-view tasks. Lines are indeed well-defined by the image gradient, frequently appear even in poorly textured areas and offer robust structural cues. We thus hereby introduce the first joint detection and description of line segments in a single deep network. Thanks to a self-supervised training, our method does not require any annotated line labels and can therefore generalize to any dataset. Our detector offers repeatable and accurate localization of line segments in images, departing from the wireframe parsing approach. Leveraging the recent progresses in descriptor learning, our proposed line descriptor is highly discriminative, while remaining robust to viewpoint changes and occlusions. We evaluate our approach against previous line detection and description methods on several multi-view datasets created with homographic warps as well as real-world viewpoint changes. Our full pipeline yields higher repeatability, localization accuracy and matching metrics, and thus represents a first step to bridge the gap with learned feature points methods. Code and trained weights are available at https://github.com/cvg/SOLD2. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_Line_Description_and_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03362 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_Line_Description_and_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_Line_Description_and_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_CVPR_2021_supplemental.pdf | null |
Shared Cross-Modal Trajectory Prediction for Autonomous Driving | Chiho Choi, Joon Hee Choi, Jiachen Li, Srikanth Malla | Predicting future trajectories of traffic agents in highly interactive environments is an essential and challenging problem for the safe operation of autonomous driving systems. On the basis of the fact that self-driving vehicles are equipped with various types of sensors (e.g., LiDAR scanner, RGB camera, radar, etc.), we propose a Cross-Modal Embedding framework that aims to benefit from the use of multiple input modalities. At training time, our model learns to embed a set of complementary features in a shared latent space by jointly optimizing the objective functions across different types of input data. At test time, a single input modality (e.g., LiDAR data) is required to generate predictions from the input perspective (i.e., in the LiDAR space), while taking advantages from the model trained with multiple sensor modalities. An extensive evaluation is conducted to show the efficacy of the proposed framework using two benchmark driving datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_Shared_Cross-Modal_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Shared_Cross-Modal_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Shared_Cross-Modal_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_Shared_Cross-Modal_Trajectory_CVPR_2021_supplemental.zip | null |
Cycle4Completion: Unpaired Point Cloud Completion Using Cycle Transformation With Missing Region Coding | Xin Wen, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Yu-Shen Liu | In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired completion methods merely focus on the learning of geometric correspondence from incomplete shapes to complete shapes, and ignore the learning in the reverse direction, which makes them suffer from low completion accuracy due to the limited 3D shape understanding ability. To address this problem, we propose two simultaneous cycle transformations between the latent spaces of complete shapes and incomplete ones. Specifically, the first cycle transforms shapes from incomplete domain to complete domain, and then projects them back to the incomplete domain. This process learns the geometric characteristic of complete shapes, and maintains the shape consistency between the complete prediction and the incomplete input. Similarly, the inverse cycle transformation starts from complete domain to incomplete domain, and goes back to complete domain to learn the characteristic of incomplete shapes. We experimentally show that our model with the learned bidirectional geometry correspondence outperforms state-of-the-art unpaired completion methods. Code will be available at https://github.com/diviswen/Cycle4Completion. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Cycle4Completion_Unpaired_Point_Cloud_Completion_Using_Cycle_Transformation_With_Missing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07838 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Cycle4Completion_Unpaired_Point_Cloud_Completion_Using_Cycle_Transformation_With_Missing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Cycle4Completion_Unpaired_Point_Cloud_Completion_Using_Cycle_Transformation_With_Missing_CVPR_2021_paper.html | CVPR 2021 | null | null |
CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation | Tao Lu, Limin Wang, Gangshan Wu | Previous point cloud semantic segmentation networks use the same process to aggregate features from neighbors of the same category and different categories. However, the joint area between two objects usually only occupies a small percentage in the whole scene. Thus the networks are well-trained for aggregating features from the same category point while not fully trained on aggregating points of different categories. To address this issue, this paper proposes to utilize different aggregation strategies between the same category and different categories. Specifically, it presents a customized module, termed as Category Guided Aggregation (CGA), where it first identifies whether the neighbors belong to the same category with the center point or not, and then handles the two types of neighbors with two carefully-designed modules. Our CGA presents a general network module and could be leveraged in any existing semantic segmentation network. Experiments on three different backbones demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_CGA-Net_Category_Guided_Aggregation_for_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_CGA-Net_Category_Guided_Aggregation_for_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_CGA-Net_Category_Guided_Aggregation_for_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
PLOP: Learning Without Forgetting for Continual Semantic Segmentation | Arthur Douillard, Yifu Chen, Arnaud Dapogny, Matthieu Cord | Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segmentation (CSS) is an emerging trend that consists in updating an old model by sequentially adding new classes. However, continual learning methods are usually prone to catastrophic forgetting. This issue is further aggravated in CSS where, at each step, old classes from previous iterations are collapsed into the background. In this paper, we propose Local POD, a multi-scale pooling distillation scheme that preserves long- and short-range spatial relationships at feature level. Furthermore, we design an entropy-based pseudo-labelling of the background w.r.t. classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes. Our approach, called PLOP, significantly outperforms state-of-the-art methods in existing CSS scenarios, as well as in newly proposed challenging benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Douillard_PLOP_Learning_Without_Forgetting_for_Continual_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11390 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Douillard_PLOP_Learning_Without_Forgetting_for_Continual_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Douillard_PLOP_Learning_Without_Forgetting_for_Continual_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Douillard_PLOP_Learning_Without_CVPR_2021_supplemental.pdf | null |
Magic Layouts: Structural Prior for Component Detection in User Interface Designs | Dipu Manandhar, Hailin Jin, John Collomosse | We present Magic Layouts; a method for parsing screenshots or hand-drawn sketches of user interface (UI) layouts. Our core contribution is to extend existing detectors to exploit a learned structural prior for UI designs, enabling robust detection of UI components; buttons, text boxes and similar. Specifically we learn a prior over mobile UI layouts, encoding common spatial co-occurrence relationships between different UI components. Conditioning region proposals using this prior leads to performance gains on UI layout parsing for both hand-drawn UIs and app screenshots, which we demonstrate within the context an interactive application for rapidly acquiring digital prototypes of user experience (UX) designs. | https://openaccess.thecvf.com/content/CVPR2021/papers/Manandhar_Magic_Layouts_Structural_Prior_for_Component_Detection_in_User_Interface_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.07615 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Manandhar_Magic_Layouts_Structural_Prior_for_Component_Detection_in_User_Interface_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Manandhar_Magic_Layouts_Structural_Prior_for_Component_Detection_in_User_Interface_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Manandhar_Magic_Layouts_Structural_CVPR_2021_supplemental.zip | null |
MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation | Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Zhibo Chen | For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning or by explicitly aligning their statistics. However, the optimization objective of such domain alignment is generally not coordinated with that of the object classification task itself such that their descent directions for optimization may be inconsistent. This will reduce the effectiveness of domain alignment in improving the performance of UDA. In this paper, we aim to study and alleviate the optimization inconsistency problem between the domain alignment and classification tasks. We address this by proposing an effective meta-optimization based strategy dubbed MetaAlign, where we treat the domain alignment objective and the classification objective as the meta-train and meta-test tasks in a meta-learning scheme. MetaAlign encourages both tasks to be optimized in a coordinated way, which maximizes the inner product of the gradients of the two tasks during training. Experimental results demonstrate the effectiveness of our proposed method on top of various alignment-based baseline approaches, for tasks of object classification and object detection. MetaAlign helps achieve the state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wei_MetaAlign_Coordinating_Domain_Alignment_and_Classification_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13575 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_MetaAlign_Coordinating_Domain_Alignment_and_Classification_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wei_MetaAlign_Coordinating_Domain_Alignment_and_Classification_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wei_MetaAlign_Coordinating_Domain_CVPR_2021_supplemental.pdf | null |
Neural Prototype Trees for Interpretable Fine-Grained Image Recognition | Meike Nauta, Ron van Bree, Christin Seifert | Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate such models. We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image recognition. ProtoTree combines prototype learning with decision trees, and thus results in a globally interpretable model by design. Additionally, ProtoTree can locally explain a single prediction by outlining a decision path through the tree. Each node in our binary tree contains a trainable prototypical part. The presence or absence of this learned prototype in an image determines the routing through a node. Decision making is therefore similar to human reasoning: Does the bird have a red throat? And an elongated beak? Then it's a hummingbird! We tune the accuracy-interpretability trade-off using ensemble methods, pruning and binarizing. We apply pruning without sacrificing accuracy, resulting in a small tree with only 8 learned prototypes along a path to classify a bird from 200 species. An ensemble of 5 ProtoTrees achieves competitive accuracy on the CUB-200- 2011 and Stanford Cars data sets. Code is available at https://github.com/M-Nauta/ProtoTree. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.02046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nauta_Neural_Prototype_Trees_CVPR_2021_supplemental.zip | null |
Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning | Liu Bo, Qiulei Dong, Zhanyi Hu | Transductive zero-shot learning (T-ZSL) which could alleviate the domain shift problem in existing ZSL works, has received much attention recently. However, an open problem in T-ZSL: how to effectively make use of unseen-class samples for training, still remains. Addressing this problem, we first empirically analyze the roles of unseen-class samples with different degrees of hardness in the training process based on the uneven prediction phenomenon found in many ZSL methods, resulting in three observations. Then, we propose two hardness sampling approaches for selecting a subset of diverse and hard samples from a given unseen-class dataset according to these observations. The first one identifies the samples based on the class-level frequency of the model predictions while the second enhances the former by normalizing the class frequency via an approximate class prior estimated by an explored prior estimation algorithm. Finally, we design a new Self-Training framework with Hardness Sampling for T-ZSL, called STHS, where an arbitrary inductive ZSL method could be seamlessly embedded and it is iteratively trained with unseen-class samples selected by the hardness sampling approach. We introduce two typical ZSL methods into the STHS framework and extensive experiments demonstrate that the derived T-ZSL methods outperform many state-of-the-art methods on three public benchmarks. Besides, we note that the unseen-class dataset is separately used for training in some existing transductive generalized ZSL (T-GZSL) methods, which is not strict for a GZSL task. Hence, we suggest a more strict T-GZSL data setting and establish a competitive baseline on this setting by introducing the proposed STHS framework to T-GZSL. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bo_Hardness_Sampling_for_Self-Training_Based_Transductive_Zero-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.00264 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bo_Hardness_Sampling_for_Self-Training_Based_Transductive_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bo_Hardness_Sampling_for_Self-Training_Based_Transductive_Zero-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bo_Hardness_Sampling_for_CVPR_2021_supplemental.pdf | null |
Hilbert Sinkhorn Divergence for Optimal Transport | Qian Li, Zhichao Wang, Gang Li, Jun Pang, Guandong Xu | Sinkhorn divergence has become a very popular metric to compare probability distributions in optimal transport. However, most works resort to Sinkhorn divergence in Euclidean space, which greatly blocks their applications in complex data with nonlinear structure. It is therefore of theoretical demand to empower Sinkhorn divergence with the capability of capturing nonlinear structures. We propose a theoretical and computational framework to bridge this gap. In this paper, we extend Sinkhorn divergence in Euclidean space to the reproducing kernel Hilbert space, which we term "Hilbert Sinkhorn divergence" (HSD).In particular, we can use kernel matrices to derive a closed form expression of HSD that is proved to be a tractable convex optimization problem. We also prove several attractive statistical properties of the proposed HSD, i.e., strong consistency, asymptotic behavior and sample complexity. Empirically, our method yields state-of-the-art performances on image classification and topological data analysis. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Hilbert_Sinkhorn_Divergence_for_Optimal_Transport_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Hilbert_Sinkhorn_Divergence_for_Optimal_Transport_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Hilbert_Sinkhorn_Divergence_for_Optimal_Transport_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Hilbert_Sinkhorn_Divergence_CVPR_2021_supplemental.pdf | null |
The Multi-Temporal Urban Development SpaceNet Dataset | Adam Van Etten, Daniel Hogan, Jesus Martinez Manso, Jacob Shermeyer, Nicholas Weir, Ryan Lewis | Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 231 United Nations Sustainable Development Goals Indicators, but the World Bank estimates that over 100 countries currently lack effective Civil Registration systems. To help address this deficit and develop novel computer vision methods for time series data, we present the Multi-Temporal Urban Development SpaceNet (MUDS, also known as SpaceNet 7) dataset. This open source dataset consists of medium resolution (4.0m) satellite imagery mosaics, which includes 24 images (one per month) covering >100 unique geographies, and comprises >40,000 km2 of imagery and exhaustive polygon labels of building footprints therein, totaling over 11M individual annotations. Each building is assigned a unique identifier (i.e. address), which permits tracking of individual objects over time. Label fidelity exceeds image resolution; this "omniscient labeling" is a unique feature of the dataset, and enables surprisingly precise algorithmic models to be crafted. We demonstrate methods to track building footprint construction (or demolition) over time, thereby directly assessing urbanization. Performance is measured with the newly developed SpaceNet Change and Object Tracking (SCOT) metric, which quantifies both object tracking as well as change detection. We demonstrate that despite the moderate resolution of the data, we are able to track individual building identifiers over time. | https://openaccess.thecvf.com/content/CVPR2021/papers/Van_Etten_The_Multi-Temporal_Urban_Development_SpaceNet_Dataset_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Van_Etten_The_Multi-Temporal_Urban_Development_SpaceNet_Dataset_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Van_Etten_The_Multi-Temporal_Urban_Development_SpaceNet_Dataset_CVPR_2021_paper.html | CVPR 2021 | null | null |
FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining | Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, Joseph E. Gonzalez | Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. However, previous NAS methods search for architectures under one set of training hyper-parameters (i.e., a training recipe), overlooking superior architecture-recipe combinations. To address this, we present Neural Architecture-Recipe Search (NARS) to search both (a) architectures and (b) their corresponding training recipes, simultaneously. NARS utilizes an accuracy predictor that scores architecture and training recipes jointly, guiding both sample selection and ranking. Furthermore, to compensate for the enlarged search space, we leverage "free" architecture statistics (e.g., FLOP count) to pretrain the predictor, significantly improving its sample efficiency and prediction reliability. After training the predictor via constrained iterative optimization, we run fast evolutionary searches in just CPU minutes to generate architecture-recipe pairs for a variety of resource constraints, called FBNetV3. FBNetV3 makes up a family of state-of-the-art compact neural networks that outperform both automatically and manually-designed competitors. For example, FBNetV3 matches both EfficientNet and ResNeSt accuracy on ImageNet with up to 2.0x and 7.1x fewer FLOPs, respectively. Furthermore, FBNetV3 yields significant performance gains for downstream object detection tasks, improving mAP despite 18% fewer FLOPs and 34% fewer parameters than EfficientNet-based equivalents. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_FBNetV3_Joint_Architecture-Recipe_Search_Using_Predictor_Pretraining_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.02049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_FBNetV3_Joint_Architecture-Recipe_Search_Using_Predictor_Pretraining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_FBNetV3_Joint_Architecture-Recipe_Search_Using_Predictor_Pretraining_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_FBNetV3_Joint_Architecture-Recipe_CVPR_2021_supplemental.pdf | null |
Intrinsic Image Harmonization | Zonghui Guo, Haiyong Zheng, Yufeng Jiang, Zhaorui Gu, Bing Zheng | Compositing an image usually inevitably suffers from inharmony problem that is mainly caused by incompatibility of foreground and background from two different images with distinct surfaces and lights, corresponding to material-dependent and light-dependent characteristics, namely, reflectance and illumination intrinsic images, respectively. Therefore, we seek to solve image harmonization via separable harmonization of reflectance and illumination, i.e., intrinsic image harmonization. Our method is based on an autoencoder that disentangles composite image into reflectance and illumination for further separate harmonization. Specifically, we harmonize reflectance through material-consistency penalty, while harmonize illumination by learning and transferring light from background to foreground, moreover, we model patch relations between foreground and background of composite images in an inharmony-free learning way, to adaptively guide our intrinsic image harmonization. Both extensive experiments and ablation studies demonstrate the power of our method as well as the efficacy of each component. We also contribute a new challenging dataset for benchmarking illumination harmonization. Code and dataset are at https://github.com/zhenglab/IntrinsicHarmony. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Intrinsic_Image_Harmonization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Intrinsic_Image_Harmonization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Intrinsic_Image_Harmonization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guo_Intrinsic_Image_Harmonization_CVPR_2021_supplemental.pdf | null |
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