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title string | authors string | abstract string | pdf string | arXiv string | bibtex string | url string | detail_url string | tags string | supp string | string |
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Rethinking Semantic Segmentation From a Sequence-to-Sequence Perspective With Transformers | Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip H.S. Torr, Li Zhang | Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract/semantic visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the late... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Rethinking_Semantic_Segmentation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.15840 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Rethinking_Semantic_Segmentation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Rethinking_Semantic_Segmentation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Rethinking_Semantic_Segmentation_CVPR_2021_supplemental.pdf | null |
Interpreting Super-Resolution Networks With Local Attribution Maps | Jinjin Gu, Chao Dong | Image super-resolution (SR) techniques have been developing rapidly, benefiting from the invention of deep networks and its successive breakthroughs. However, it is acknowledged that deep learning and deep neural networks are difficult to interpret. SR networks inherit this mysterious nature and little works make attem... | https://openaccess.thecvf.com/content/CVPR2021/papers/Gu_Interpreting_Super-Resolution_Networks_With_Local_Attribution_Maps_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11036 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gu_Interpreting_Super-Resolution_Networks_With_Local_Attribution_Maps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gu_Interpreting_Super-Resolution_Networks_With_Local_Attribution_Maps_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gu_Interpreting_Super-Resolution_Networks_CVPR_2021_supplemental.pdf | null |
Multi-Target Domain Adaptation With Collaborative Consistency Learning | Takashi Isobe, Xu Jia, Shuaijun Chen, Jianzhong He, Yongjie Shi, Jianzhuang Liu, Huchuan Lu, Shengjin Wang | Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to the high-cost of pixel-level annotation on real-world images. However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extended to multiple t... | https://openaccess.thecvf.com/content/CVPR2021/papers/Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.03418 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Troubleshooting Blind Image Quality Models in the Wild | Zhihua Wang, Haotao Wang, Tianlong Chen, Zhangyang Wang, Kede Ma | Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When applying this type of approach to troubleshoot "best-performing" BIQA models in the wild, we are faced with a practical challenge: it is hig... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Troubleshooting_Blind_Image_Quality_Models_in_the_Wild_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.06747 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Troubleshooting_Blind_Image_Quality_Models_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Troubleshooting_Blind_Image_Quality_Models_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | null | null |
Semantic Palette: Guiding Scene Generation With Class Proportions | Guillaume Le Moing, Tuan-Hung Vu, Himalaya Jain, Patrick Perez, Matthieu Cord | Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem. Previous works break down scene generation into two consecutive phases: unconditional semantic layout synthesis and image synthesis conditioned on l... | https://openaccess.thecvf.com/content/CVPR2021/papers/Le_Moing_Semantic_Palette_Guiding_Scene_Generation_With_Class_Proportions_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.01629 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Le_Moing_Semantic_Palette_Guiding_Scene_Generation_With_Class_Proportions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Le_Moing_Semantic_Palette_Guiding_Scene_Generation_With_Class_Proportions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Le_Moing_Semantic_Palette_Guiding_CVPR_2021_supplemental.pdf | null |
Physics-Based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging | Feilong Zhang, Xianming Liu, Cheng Guo, Shiyi Lin, Junjun Jiang, Xiangyang Ji | Phase retrieval from intensity-only measurements plays a central role in many real-world imaging tasks. In recent years, deep neural networks based methods emerge and show promising performance for phase retrieval. However, their interpretability and generalization still remain a major challenge. In this paper, we prop... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Physics-Based_Iterative_Projection_Complex_Neural_Network_for_Phase_Retrieval_in_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Physics-Based_Iterative_Projection_Complex_Neural_Network_for_Phase_Retrieval_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Physics-Based_Iterative_Projection_Complex_Neural_Network_for_Phase_Retrieval_in_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Physics-Based_Iterative_Projection_CVPR_2021_supplemental.pdf | null |
Causal Attention for Vision-Language Tasks | Xu Yang, Hanwang Zhang, Guojun Qi, Jianfei Cai | We present a novel attention mechanism: Causal Attention (CATT), to remove the ever-elusive confounding effect in existing attention-based vision-language models. This effect causes harmful bias that misleads the attention module to focus on the spurious correlations in training data, damaging the model generalization.... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Causal_Attention_for_Vision-Language_Tasks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.03493 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Causal_Attention_for_Vision-Language_Tasks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Causal_Attention_for_Vision-Language_Tasks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Causal_Attention_for_CVPR_2021_supplemental.pdf | null |
Scene Text Telescope: Text-Focused Scene Image Super-Resolution | Jingye Chen, Bin Li, Xiangyang Xue | Image super-resolution, which is often regarded as a preprocessing procedure of scene text recognition, aims to recover the realistic features from a low-resolution text image. It has always been challenging due to large variations in text shapes, fonts, backgrounds, etc. However, most existing methods employ generic s... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Scene_Text_Telescope_CVPR_2021_supplemental.pdf | null |
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering | Fanbo Xiang, Zexiang Xu, Milos Hasan, Yannick Hold-Geoffroy, Kalyan Sunkavalli, Hao Su | Recent work has demonstrated that volumetric scene representations combined with differentiable volume rendering can enable photo-realistic rendering for challenging scenes that mesh reconstruction fails on. However, these methods entangle geometry and appearance in a ""black-box"" volume that cannot be edited. Instead... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiang_NeuTex_Neural_Texture_Mapping_for_Volumetric_Neural_Rendering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.00762 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xiang_NeuTex_Neural_Texture_Mapping_for_Volumetric_Neural_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xiang_NeuTex_Neural_Texture_Mapping_for_Volumetric_Neural_Rendering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiang_NeuTex_Neural_Texture_CVPR_2021_supplemental.zip | null |
Improving Calibration for Long-Tailed Recognition | Zhisheng Zhong, Jiequan Cui, Shu Liu, Jiaya Jia | Deep neural networks may perform poorly when training datasets are heavily class-imbalanced. Recently, two-stage methods decouple representation learning and classifier learning to improve performance. But there is still the vital issue of miscalibration. To address it, we design two methods to improve calibration and ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_Improving_Calibration_for_Long-Tailed_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00466 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Improving_Calibration_for_Long-Tailed_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Improving_Calibration_for_Long-Tailed_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_Improving_Calibration_for_CVPR_2021_supplemental.pdf | null |
Learning Affinity-Aware Upsampling for Deep Image Matting | Yutong Dai, Hao Lu, Chunhua Shen | We show that learning affinity in upsampling provides an effective and efficient approach to exploit pairwise interactions in deep networks. Second-order features are commonly used in dense prediction to build adjacent relations with a learnable module after upsampling such as non-local blocks. Since upsampling is esse... | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Learning_Affinity-Aware_Upsampling_for_Deep_Image_Matting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14288 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_Learning_Affinity-Aware_Upsampling_for_Deep_Image_Matting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_Learning_Affinity-Aware_Upsampling_for_Deep_Image_Matting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_Learning_Affinity-Aware_Upsampling_CVPR_2021_supplemental.pdf | null |
Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling | Daniel Stadler, Jurgen Beyerer | Multi-pedestrian trackers perform well when targets are clearly visible making the association task quite easy. However, when heavy occlusions are present, a mechanism to reidentify persons is needed. The common approach is to extract visual features from new detections and compare them with the features of previously ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Stadler_Improving_Multiple_Pedestrian_Tracking_by_Track_Management_and_Occlusion_Handling_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Stadler_Improving_Multiple_Pedestrian_Tracking_by_Track_Management_and_Occlusion_Handling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Stadler_Improving_Multiple_Pedestrian_Tracking_by_Track_Management_and_Occlusion_Handling_CVPR_2021_paper.html | CVPR 2021 | null | null |
Revamping Cross-Modal Recipe Retrieval With Hierarchical Transformers and Self-Supervised Learning | Amaia Salvador, Erhan Gundogdu, Loris Bazzani, Michael Donoser | Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people's lives, as well as the availability of vast amounts of digital cooking recipes and food images to train machine learning models. In this work, we revisit existing approaches for cross-modal recipe retrieval a... | https://openaccess.thecvf.com/content/CVPR2021/papers/Salvador_Revamping_Cross-Modal_Recipe_Retrieval_With_Hierarchical_Transformers_and_Self-Supervised_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13061 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Salvador_Revamping_Cross-Modal_Recipe_Retrieval_With_Hierarchical_Transformers_and_Self-Supervised_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Salvador_Revamping_Cross-Modal_Recipe_Retrieval_With_Hierarchical_Transformers_and_Self-Supervised_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Salvador_Revamping_Cross-Modal_Recipe_CVPR_2021_supplemental.pdf | null |
Geo-FARM: Geodesic Factor Regression Model for Misaligned Pre-Shape Responses in Statistical Shape Analysis | Chao Huang, Anuj Srivastava, Rongjie Liu | The problem of using covariates to predict shapes of objects in a regression setting is important in many fields. A formal statistical approach, termed geodesic regression model, is commonly used for modeling and analyzing relationships between Euclidean predictors and shape responses. Despite its popularity, this mode... | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Geo-FARM_Geodesic_Factor_Regression_Model_for_Misaligned_Pre-Shape_Responses_in_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Geo-FARM_Geodesic_Factor_Regression_Model_for_Misaligned_Pre-Shape_Responses_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Geo-FARM_Geodesic_Factor_Regression_Model_for_Misaligned_Pre-Shape_Responses_in_CVPR_2021_paper.html | CVPR 2021 | null | null |
MOST: A Multi-Oriented Scene Text Detector With Localization Refinement | Minghang He, Minghui Liao, Zhibo Yang, Humen Zhong, Jun Tang, Wenqing Cheng, Cong Yao, Yongpan Wang, Xiang Bai | Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios. However, they might still fall short when handling text instances of extreme aspect ratios and varying scales. To tackle such difficulties, we propose in t... | https://openaccess.thecvf.com/content/CVPR2021/papers/He_MOST_A_Multi-Oriented_Scene_Text_Detector_With_Localization_Refinement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01070 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_MOST_A_Multi-Oriented_Scene_Text_Detector_With_Localization_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_MOST_A_Multi-Oriented_Scene_Text_Detector_With_Localization_Refinement_CVPR_2021_paper.html | CVPR 2021 | null | null |
A Functional Approach to Rotation Equivariant Non-Linearities for Tensor Field Networks. | Adrien Poulenard, Leonidas J. Guibas | Learning pose invariant representation is a fundamental problem in shape analysis. Most existing deep learning algorithms for 3D shape analysis are not robust to rotations and are often trained on synthetic datasets consisting of pre-aligned shapes, yielding poor generalization to unseen poses. This observation motivat... | https://openaccess.thecvf.com/content/CVPR2021/papers/Poulenard_A_Functional_Approach_to_Rotation_Equivariant_Non-Linearities_for_Tensor_Field_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Poulenard_A_Functional_Approach_to_Rotation_Equivariant_Non-Linearities_for_Tensor_Field_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Poulenard_A_Functional_Approach_to_Rotation_Equivariant_Non-Linearities_for_Tensor_Field_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Poulenard_A_Functional_Approach_CVPR_2021_supplemental.pdf | null |
Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms | Yuxing Tang, Zhenjie Cao, Yanbo Zhang, Zhicheng Yang, Zongcheng Ji, Yiwei Wang, Mei Han, Jie Ma, Jing Xiao, Peng Chang | Mammographic mass detection is an integral part of a computer-aided diagnosis system. Annotating a large number of mammograms at pixel-level in order to train a mass detection model in a fully supervised fashion is costly and time-consuming. This paper presents a novel self-training framework for semi-supervised mass d... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Leveraging_Large-Scale_Weakly_Labeled_Data_for_Semi-Supervised_Mass_Detection_in_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Leveraging_Large-Scale_Weakly_Labeled_Data_for_Semi-Supervised_Mass_Detection_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Leveraging_Large-Scale_Weakly_Labeled_Data_for_Semi-Supervised_Mass_Detection_in_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Leveraging_Large-Scale_Weakly_CVPR_2021_supplemental.pdf | null |
Fast and Accurate Model Scaling | Piotr Dollar, Mannat Singh, Ross Girshick | In this work we analyze strategies for convolutional neural network scaling; that is, the process of scaling a base convolutional network to endow it with greater computational complexity and consequently representational power. Example scaling strategies may include increasing model width, depth, resolution, etc. Whil... | https://openaccess.thecvf.com/content/CVPR2021/papers/Dollar_Fast_and_Accurate_Model_Scaling_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06877 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dollar_Fast_and_Accurate_Model_Scaling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dollar_Fast_and_Accurate_Model_Scaling_CVPR_2021_paper.html | CVPR 2021 | null | null |
Real-Time Sphere Sweeping Stereo From Multiview Fisheye Images | Andreas Meuleman, Hyeonjoong Jang, Daniel S. Jeon, Min H. Kim | A set of cameras with fisheye lenses have been used to capture a wide field of view. The traditional scan-line stereo algorithms based on epipolar geometry are directly inapplicable to this non-pinhole camera setup due to optical characteristics of fisheye lenses; hence, existing complete 360-deg. RGB-D imaging systems... | https://openaccess.thecvf.com/content/CVPR2021/papers/Meuleman_Real-Time_Sphere_Sweeping_Stereo_From_Multiview_Fisheye_Images_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Meuleman_Real-Time_Sphere_Sweeping_Stereo_From_Multiview_Fisheye_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Meuleman_Real-Time_Sphere_Sweeping_Stereo_From_Multiview_Fisheye_Images_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meuleman_Real-Time_Sphere_Sweeping_CVPR_2021_supplemental.zip | null |
Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework | Qiang Zhou, Chaohui Yu, Zhibin Wang, Qi Qian, Hao Li | Supervised learning based object detection frameworks demand plenty of laborious manual annotations, which may not be practical in real applications. Semi-supervised object detection (SSOD) can effectively leverage unlabeled data to improve the model performance, which is of great significance for the application of ob... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Instant-Teaching_An_End-to-End_Semi-Supervised_Object_Detection_Framework_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Instant-Teaching_An_End-to-End_Semi-Supervised_Object_Detection_Framework_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Instant-Teaching_An_End-to-End_Semi-Supervised_Object_Detection_Framework_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Instant-Teaching_An_End-to-End_CVPR_2021_supplemental.pdf | null |
Taskology: Utilizing Task Relations at Scale | Yao Lu, Soren Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad, Zhao Chen, Vincent Casser, Anelia Angelova, Ariel Gordon | Many computer vision tasks address the problem of scene understanding and are naturally interrelated e.g. object classification, detection, scene segmentation, depth estimation, etc. We show that we can leverage the inherent relationships among collections of tasks, as they are trained jointly, supervising each other t... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Taskology_Utilizing_Task_Relations_at_Scale_CVPR_2021_paper.pdf | http://arxiv.org/abs/2005.07289 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Taskology_Utilizing_Task_Relations_at_Scale_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Taskology_Utilizing_Task_Relations_at_Scale_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_Taskology_Utilizing_Task_CVPR_2021_supplemental.pdf | null |
Progressive Domain Expansion Network for Single Domain Generalization | Lei Li, Ke Gao, Juan Cao, Ziyao Huang, Yepeng Weng, Xiaoyue Mi, Zhengze Yu, Xiaoya Li, Boyang Xia | Single domain generalization is a challenging case of model generalization, where the models are trained on a single domain and tested on other unseen domains. A promising solution is to learn cross-domain invariant representations by expanding the coverage of the training domain. These methods have limited generalizat... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Progressive_Domain_Expansion_Network_for_Single_Domain_Generalization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16050 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Progressive_Domain_Expansion_Network_for_Single_Domain_Generalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Progressive_Domain_Expansion_Network_for_Single_Domain_Generalization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Progressive_Domain_Expansion_CVPR_2021_supplemental.pdf | null |
View-Guided Point Cloud Completion | Xuancheng Zhang, Yutong Feng, Siqi Li, Changqing Zou, Hai Wan, Xibin Zhao, Yandong Guo, Yue Gao | This paper presents a view-guided solution for the task of point cloud completion. Unlike most existing methods directly inferring the missing points using shape priors, we address this task by introducing ViPC (view-guided point cloud completion) that takes the missing crucial global structure information from an extr... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_View-Guided_Point_Cloud_Completion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05666 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_View-Guided_Point_Cloud_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_View-Guided_Point_Cloud_Completion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_View-Guided_Point_Cloud_CVPR_2021_supplemental.pdf | null |
Generative Hierarchical Features From Synthesizing Images | Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, Bolei Zhou | Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data. However, how the features learned from solving the task of image generation are applicable to other vision tasks remains seldom explored. In this work, we show that learning to syn... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Generative_Hierarchical_Features_From_Synthesizing_Images_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.10379 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Generative_Hierarchical_Features_From_Synthesizing_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Generative_Hierarchical_Features_From_Synthesizing_Images_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Generative_Hierarchical_Features_CVPR_2021_supplemental.pdf | null |
Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality | Trisha Mittal, Puneet Mathur, Aniket Bera, Dinesh Manocha | We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas from emotion causation theories to computationally model and determine... | https://openaccess.thecvf.com/content/CVPR2021/papers/Mittal_Affect2MM_Affective_Analysis_of_Multimedia_Content_Using_Emotion_Causality_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06541 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mittal_Affect2MM_Affective_Analysis_of_Multimedia_Content_Using_Emotion_Causality_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mittal_Affect2MM_Affective_Analysis_of_Multimedia_Content_Using_Emotion_Causality_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mittal_Affect2MM_Affective_Analysis_CVPR_2021_supplemental.pdf | null |
Black-Box Explanation of Object Detectors via Saliency Maps | Vitali Petsiuk, Rajiv Jain, Varun Manjunatha, Vlad I. Morariu, Ashutosh Mehra, Vicente Ordonez, Kate Saenko | We propose D-RISE, a method for generating visual explanations for the predictions of object detectors. Utilizing the proposed similarity metric that accounts for both localization and categorization aspects of object detection allows our method to produce saliency maps that show image areas that most affect the predic... | https://openaccess.thecvf.com/content/CVPR2021/papers/Petsiuk_Black-Box_Explanation_of_Object_Detectors_via_Saliency_Maps_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.03204 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Petsiuk_Black-Box_Explanation_of_Object_Detectors_via_Saliency_Maps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Petsiuk_Black-Box_Explanation_of_Object_Detectors_via_Saliency_Maps_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Petsiuk_Black-Box_Explanation_of_CVPR_2021_supplemental.pdf | null |
Skip-Convolutions for Efficient Video Processing | Amirhossein Habibian, Davide Abati, Taco S. Cohen, Babak Ehteshami Bejnordi | We propose Skip-Convolutions to leverage the large amount of redundancies in video streams and save computations. Each video is represented as a series of changes across frames and network activations, denoted as residuals. We reformulate standard convolution to be efficiently computed on residual frames: each layer is... | https://openaccess.thecvf.com/content/CVPR2021/papers/Habibian_Skip-Convolutions_for_Efficient_Video_Processing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11487 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Habibian_Skip-Convolutions_for_Efficient_Video_Processing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Habibian_Skip-Convolutions_for_Efficient_Video_Processing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Habibian_Skip-Convolutions_for_Efficient_CVPR_2021_supplemental.pdf | null |
Looking Into Your Speech: Learning Cross-Modal Affinity for Audio-Visual Speech Separation | Jiyoung Lee, Soo-Whan Chung, Sunok Kim, Hong-Goo Kang, Kwanghoon Sohn | In this paper, we address the problem of separating individual speech signals from videos using audio-visual neural processing. Most conventional approaches utilize frame-wise matching criteria to extract shared information between co-occurring audio and video. Thus, their performance heavily depends on the accuracy of... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Looking_Into_Your_Speech_Learning_Cross-Modal_Affinity_for_Audio-Visual_Speech_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02775 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Looking_Into_Your_Speech_Learning_Cross-Modal_Affinity_for_Audio-Visual_Speech_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Looking_Into_Your_Speech_Learning_Cross-Modal_Affinity_for_Audio-Visual_Speech_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Looking_Into_Your_CVPR_2021_supplemental.pdf | null |
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution | Kelvin C.K. Chan, Xintao Wang, Xiangyu Xu, Jinwei Gu, Chen Change Loy | We show that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR). While most existing SR approaches attempt to generate realistic textures through learning with adversarial loss, our method, Gener... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chan_GLEAN_Generative_Latent_Bank_for_Large-Factor_Image_Super-Resolution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00739 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chan_GLEAN_Generative_Latent_Bank_for_Large-Factor_Image_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chan_GLEAN_Generative_Latent_Bank_for_Large-Factor_Image_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chan_GLEAN_Generative_Latent_CVPR_2021_supplemental.pdf | null |
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective | Jingwei Sun, Ang Li, Binghui Wang, Huanrui Yang, Hai Li, Yiran Chen | Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. However, recent works have demonstrated that sharing model updates makes FL vulnerable to inference attack. In this work, we show our key observation that the data representation lea... | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Soteria_Provable_Defense_CVPR_2021_supplemental.pdf | null |
Deep Occlusion-Aware Instance Segmentation With Overlapping BiLayers | Lei Ke, Yu-Wing Tai, Chi-Keung Tang | Segmenting highly-overlapping objects is challenging, because typically no distinction is made between real object contours and occlusion boundaries. Unlike previous two-stage instance segmentation methods, we model image formation as composition of two overlapping layers, and propose Bilayer Convolutional Network (BCN... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ke_Deep_Occlusion-Aware_Instance_Segmentation_With_Overlapping_BiLayers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ke_Deep_Occlusion-Aware_Instance_Segmentation_With_Overlapping_BiLayers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ke_Deep_Occlusion-Aware_Instance_Segmentation_With_Overlapping_BiLayers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ke_Deep_Occlusion-Aware_Instance_CVPR_2021_supplemental.pdf | null |
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments From a Single Moving Camera | Felix Wimbauer, Nan Yang, Lukas von Stumberg, Niclas Zeller, Daniel Cremers | In this paper, we propose MonoRec, a semi-supervised monocular dense reconstruction architecture that predicts depth maps from a single moving camera in dynamic environments. MonoRec is based on a multi-view stereo setting which encodes the information of multiple consecutive images in a cost volume. To deal with dynam... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wimbauer_MonoRec_Semi-Supervised_Dense_Reconstruction_in_Dynamic_Environments_From_a_Single_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11814 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wimbauer_MonoRec_Semi-Supervised_Dense_Reconstruction_in_Dynamic_Environments_From_a_Single_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wimbauer_MonoRec_Semi-Supervised_Dense_Reconstruction_in_Dynamic_Environments_From_a_Single_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wimbauer_MonoRec_Semi-Supervised_Dense_CVPR_2021_supplemental.pdf | null |
DAP: Detection-Aware Pre-Training With Weak Supervision | Yuanyi Zhong, Jianfeng Wang, Lijuan Wang, Jian Peng, Yu-Xiong Wang, Lei Zhang | This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is specifically tailored to benefit object detection tasks. In contrast to the widely used image classification-based pre-training (e.g., on ImageNe... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_DAP_Detection-Aware_Pre-Training_With_Weak_Supervision_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16651 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_DAP_Detection-Aware_Pre-Training_With_Weak_Supervision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_DAP_Detection-Aware_Pre-Training_With_Weak_Supervision_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_DAP_Detection-Aware_Pre-Training_CVPR_2021_supplemental.pdf | null |
Spatial Assembly Networks for Image Representation Learning | Yang Li, Shichao Kan, Jianhe Yuan, Wenming Cao, Zhihai He | It has been long recognized that deep neural networks are sensitive to changes in spatial configurations or scene structures. Image augmentations, such as random translation, cropping, and resizing, can be used to improve the robustness of deep neural networks under spatial transforms. However, changes in object part c... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Spatial_Assembly_Networks_for_Image_Representation_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Spatial_Assembly_Networks_for_Image_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Spatial_Assembly_Networks_for_Image_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Spatial_Assembly_Networks_CVPR_2021_supplemental.pdf | null |
Linguistic Structures As Weak Supervision for Visual Scene Graph Generation | Keren Ye, Adriana Kovashka | Prior work in scene graph generation requires categorical supervision at the level of triplets---subjects and objects, and predicates that relate them, either with or without bounding box information. However, scene graph generation is a holistic task: thus holistic, contextual supervision should intuitively improve pe... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_Linguistic_Structures_As_Weak_Supervision_for_Visual_Scene_Graph_Generation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.13994 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Linguistic_Structures_As_Weak_Supervision_for_Visual_Scene_Graph_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Linguistic_Structures_As_Weak_Supervision_for_Visual_Scene_Graph_Generation_CVPR_2021_paper.html | CVPR 2021 | null | null |
SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature | Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang | The bottleneck of computation burden limits the widespread use of the 2nd order optimization algorithms for training deep neural networks. In this paper, we present a computationally efficient approximation for natural gradient descent, named Swift Kronecker-Factored Approximate Curvature (SKFAC), which combines Kronec... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_SKFAC_Training_Neural_Networks_With_Faster_Kronecker-Factored_Approximate_Curvature_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_SKFAC_Training_Neural_Networks_With_Faster_Kronecker-Factored_Approximate_Curvature_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_SKFAC_Training_Neural_Networks_With_Faster_Kronecker-Factored_Approximate_Curvature_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_SKFAC_Training_Neural_CVPR_2021_supplemental.zip | null |
Global2Local: Efficient Structure Search for Video Action Segmentation | Shang-Hua Gao, Qi Han, Zhong-Yu Li, Pai Peng, Liang Wang, Ming-Ming Cheng | Temporal receptive fields of models play an important role in action segmentation. Large receptive fields facilitate the long-term relations among video clips while small receptive fields help capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Global2Local_Efficient_Structure_Search_for_Video_Action_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.00910 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Global2Local_Efficient_Structure_Search_for_Video_Action_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Global2Local_Efficient_Structure_Search_for_Video_Action_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gao_Global2Local_Efficient_Structure_CVPR_2021_supplemental.pdf | null |
Picasso: A CUDA-Based Library for Deep Learning Over 3D Meshes | Huan Lei, Naveed Akhtar, Ajmal Mian | We present Picasso, a CUDA-based library comprising novel modules for deep learning over complex real-world 3D meshes. Hierarchical neural architectures have proved effective in multi-scale feature extraction which signifies the need for fast mesh decimation. However, existing methods rely on CPU-based implementations ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lei_Picasso_A_CUDA-Based_Library_for_Deep_Learning_Over_3D_Meshes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15076 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lei_Picasso_A_CUDA-Based_Library_for_Deep_Learning_Over_3D_Meshes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lei_Picasso_A_CUDA-Based_Library_for_Deep_Learning_Over_3D_Meshes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lei_Picasso_A_CUDA-Based_CVPR_2021_supplemental.pdf | null |
DeFlow: Learning Complex Image Degradations From Unpaired Data With Conditional Flows | Valentin Wolf, Andreas Lugmayr, Martin Danelljan, Luc Van Gool, Radu Timofte | The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synthesize realistic training data by modeling noise and degradations that appear in real-world settings. We propose DeFlow, a method for learni... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wolf_DeFlow_Learning_Complex_Image_Degradations_From_Unpaired_Data_With_Conditional_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.05796 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wolf_DeFlow_Learning_Complex_Image_Degradations_From_Unpaired_Data_With_Conditional_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wolf_DeFlow_Learning_Complex_Image_Degradations_From_Unpaired_Data_With_Conditional_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wolf_DeFlow_Learning_Complex_CVPR_2021_supplemental.pdf | null |
Student-Teacher Learning From Clean Inputs to Noisy Inputs | Guanzhe Hong, Zhiyuan Mao, Xiaojun Lin, Stanley H. Chan | Feature-based student-teacher learning, a training method that encourages the student's hidden features to mimic those of the teacher network, is empirically successful in transferring the knowledge from a pre-trained teacher network to the student network. Furthermore, recent empirical results demonstrate that, the te... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_Student-Teacher_Learning_From_Clean_Inputs_to_Noisy_Inputs_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07600 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Student-Teacher_Learning_From_Clean_Inputs_to_Noisy_Inputs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Student-Teacher_Learning_From_Clean_Inputs_to_Noisy_Inputs_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Student-Teacher_Learning_From_CVPR_2021_supplemental.pdf | null |
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles | Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, Raquel Urtasun | As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth actor states as input. This does not scale and cannot identify all possible auto... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_AdvSim_Generating_Safety-Critical_Scenarios_for_Self-Driving_Vehicles_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.06549 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_AdvSim_Generating_Safety-Critical_Scenarios_for_Self-Driving_Vehicles_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_AdvSim_Generating_Safety-Critical_Scenarios_for_Self-Driving_Vehicles_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_AdvSim_Generating_Safety-Critical_CVPR_2021_supplemental.zip | null |
MoViNets: Mobile Video Networks for Efficient Video Recognition | Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong | We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference. 3D convolutional neural networks (CNNs) are accurate at video recognition but require large computation and memory budgets and do not support online inferenc... | https://openaccess.thecvf.com/content/CVPR2021/papers/Kondratyuk_MoViNets_Mobile_Video_Networks_for_Efficient_Video_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.11511 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kondratyuk_MoViNets_Mobile_Video_Networks_for_Efficient_Video_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kondratyuk_MoViNets_Mobile_Video_Networks_for_Efficient_Video_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kondratyuk_MoViNets_Mobile_Video_CVPR_2021_supplemental.pdf | null |
IBRNet: Learning Multi-View Image-Based Rendering | Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P. Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser | We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views. The core of our method is a network architecture that includes a multilayer perceptron and a ray transformer that estimates radiance and volume density at continuous 5D locations (3D spatial locations and 2... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_IBRNet_Learning_Multi-View_Image-Based_Rendering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.13090 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_IBRNet_Learning_Multi-View_Image-Based_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_IBRNet_Learning_Multi-View_Image-Based_Rendering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_IBRNet_Learning_Multi-View_CVPR_2021_supplemental.pdf | null |
SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning | Colorado J Reed, Sean Metzger, Aravind Srinivas, Trevor Darrell, Kurt Keutzer | A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training process such as selecting the data augmentation policy. However, guiding an unsupervised training pro... | https://openaccess.thecvf.com/content/CVPR2021/papers/Reed_SelfAugment_Automatic_Augmentation_Policies_for_Self-Supervised_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.07724 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Reed_SelfAugment_Automatic_Augmentation_Policies_for_Self-Supervised_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Reed_SelfAugment_Automatic_Augmentation_Policies_for_Self-Supervised_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reed_SelfAugment_Automatic_Augmentation_CVPR_2021_supplemental.pdf | null |
Adversarial Invariant Learning | Nanyang Ye, Jingxuan Tang, Huayu Deng, Xiao-Yun Zhou, Qianxiao Li, Zhenguo Li, Guang-Zhong Yang, Zhanxing Zhu | Though machine learning algorithms are able to achieve pattern recognition from the correlation between data and labels, the presence of spurious features in the data decreases the robustness of these learned relationships with respect to varied testing environments. This is known as out-of-distribution (OoD) generaliz... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_Adversarial_Invariant_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Adversarial_Invariant_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Adversarial_Invariant_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ye_Adversarial_Invariant_Learning_CVPR_2021_supplemental.pdf | null |
Densely Connected Multi-Dilated Convolutional Networks for Dense Prediction Tasks | Naoya Takahashi, Yuki Mitsufuji | Tasks that involve high-resolution dense prediction require a modeling of both local and global patterns in a large input field. Although the local and global structures often depend on each other and their simultaneous modeling is important, many convolutional neural network (CNN)-based approaches interchange represen... | https://openaccess.thecvf.com/content/CVPR2021/papers/Takahashi_Densely_Connected_Multi-Dilated_Convolutional_Networks_for_Dense_Prediction_Tasks_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Takahashi_Densely_Connected_Multi-Dilated_Convolutional_Networks_for_Dense_Prediction_Tasks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Takahashi_Densely_Connected_Multi-Dilated_Convolutional_Networks_for_Dense_Prediction_Tasks_CVPR_2021_paper.html | CVPR 2021 | null | null |
Depth-Conditioned Dynamic Message Propagation for Monocular 3D Object Detection | Li Wang, Liang Du, Xiaoqing Ye, Yanwei Fu, Guodong Guo, Xiangyang Xue, Jianfeng Feng, Li Zhang | The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection. We make following contributions: (i) rather than appealing to the complicated pseudo-LiDAR based approach, we propose a depth-conditioned dynamic message propagation (DDMP) netw... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Depth-Conditioned_Dynamic_Message_Propagation_for_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16470 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Depth-Conditioned_Dynamic_Message_Propagation_for_Monocular_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Depth-Conditioned_Dynamic_Message_Propagation_for_Monocular_3D_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Depth-Conditioned_Dynamic_Message_CVPR_2021_supplemental.pdf | null |
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-Bit Neural Networks via Guided Distribution Calibration | Zhiqiang Shen, Zechun Liu, Jie Qin, Lei Huang, Kwang-Ting Cheng, Marios Savvides | Previous studies dominantly target at self-supervised learning on real-valued networks and have achieved many promising results. However, on the more challenging binary neural networks (BNNs), this task has not yet been fully explored in the community. In this paper, we focus on this more difficult scenario: learning n... | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_S2-BNN_Bridging_the_Gap_Between_Self-Supervised_Real_and_1-Bit_Neural_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_S2-BNN_Bridging_the_Gap_Between_Self-Supervised_Real_and_1-Bit_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_S2-BNN_Bridging_the_Gap_Between_Self-Supervised_Real_and_1-Bit_Neural_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Optical Flow From Still Images | Filippo Aleotti, Matteo Poggi, Stefano Mattoccia | This paper deals with the scarcity of data for training optical flow networks, highlighting the limitations of existing sources such as labeled synthetic datasets or unlabeled real videos. Specifically, we introduce a framework to generate accurate ground-truth optical flow annotations quickly and in large amounts from... | https://openaccess.thecvf.com/content/CVPR2021/papers/Aleotti_Learning_Optical_Flow_From_Still_Images_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Aleotti_Learning_Optical_Flow_From_Still_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Aleotti_Learning_Optical_Flow_From_Still_Images_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Aleotti_Learning_Optical_Flow_CVPR_2021_supplemental.pdf | null |
From Shadow Generation To Shadow Removal | Zhihao Liu, Hui Yin, Xinyi Wu, Zhenyao Wu, Yang Mi, Song Wang | Shadow removal is a computer-vision task that aims to restore the image content in shadow regions. While almost all recent shadow-removal methods require shadow-free images for training, in ECCV 2020 Le and Samaras introduces an innovative approach without this requirement by cropping patches with and without shadows f... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_From_Shadow_Generation_To_Shadow_Removal_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12997 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_From_Shadow_Generation_To_Shadow_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_From_Shadow_Generation_To_Shadow_Removal_CVPR_2021_paper.html | CVPR 2021 | null | null |
Face Forgery Detection by 3D Decomposition | Xiangyu Zhu, Hao Wang, Hongyan Fei, Zhen Lei, Stan Z. Li | Detecting digital face manipulation has attracted extensive attention due to the potential harms of fake media to the public. However, recent advances have been able to reduce the forgery signals to a low magnitude. Decomposition, which reversibly decomposes the image into several constituent elements, is a promising w... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Face_Forgery_Detection_by_3D_Decomposition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.09737 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Face_Forgery_Detection_by_3D_Decomposition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Face_Forgery_Detection_by_3D_Decomposition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_Face_Forgery_Detection_CVPR_2021_supplemental.pdf | null |
Unsupervised 3D Shape Completion Through GAN Inversion | Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, Chen Change Loy | Most 3D shape completion approaches rely heavily on partial-complete shape pairs and learn in a fully supervised manner. Despite their impressive performances on in-domain data, when generalizing to partial shapes in other forms or real-world partial scans, they often obtain unsatisfactory results due to domain gaps. I... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Unsupervised_3D_Shape_Completion_Through_GAN_Inversion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.13366 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Unsupervised_3D_Shape_Completion_Through_GAN_Inversion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Unsupervised_3D_Shape_Completion_Through_GAN_Inversion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Unsupervised_3D_Shape_CVPR_2021_supplemental.pdf | null |
Pseudo 3D Auto-Correlation Network for Real Image Denoising | Xiaowan Hu, Ruijun Ma, Zhihong Liu, Yuanhao Cai, Xiaole Zhao, Yulun Zhang, Haoqian Wang | The extraction of auto-correlation in images has shown great potential in deep learning networks, such as the self-attention mechanism in the channel domain and the self-similarity mechanism in the spatial domain. However, the realization of the above mechanisms mostly requires complicated module stacking and a large n... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Pseudo_3D_Auto-Correlation_Network_for_Real_Image_Denoising_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Pseudo_3D_Auto-Correlation_Network_for_Real_Image_Denoising_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Pseudo_3D_Auto-Correlation_Network_for_Real_Image_Denoising_CVPR_2021_paper.html | CVPR 2021 | null | null |
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training | Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu | We propose MaxUp, an embarrassingly simple, highly effective technique for improving the generalization performance of machine learning models, especially deep neural networks. The idea is to generate a set of augmented data with some random perturbations or transforms, and minimize the maximum, or worst case loss over... | https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_MaxUp_Lightweight_Adversarial_Training_With_Data_Augmentation_Improves_Neural_Network_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_MaxUp_Lightweight_Adversarial_Training_With_Data_Augmentation_Improves_Neural_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_MaxUp_Lightweight_Adversarial_Training_With_Data_Augmentation_Improves_Neural_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gong_MaxUp_Lightweight_Adversarial_CVPR_2021_supplemental.pdf | null |
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation | Jungbeom Lee, Eunji Kim, Sungroh Yoon | Weakly supervised semantic segmentation produces a pixel-level localization from class labels; but a classifier trained on such labels is likely to restrict its focus to a small discriminative region of the target object. AdvCAM is an attribution map of an image that is manipulated to increase the classification score ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Anti-Adversarially_Manipulated_Attributions_for_Weakly_and_Semi-Supervised_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.08896 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Anti-Adversarially_Manipulated_Attributions_for_Weakly_and_Semi-Supervised_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Anti-Adversarially_Manipulated_Attributions_for_Weakly_and_Semi-Supervised_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Anti-Adversarially_Manipulated_Attributions_CVPR_2021_supplemental.pdf | null |
Data-Free Knowledge Distillation for Image Super-Resolution | Yiman Zhang, Hanting Chen, Xinghao Chen, Yiping Deng, Chunjing Xu, Yunhe Wang | Convolutional network compression methods require training data for achieving acceptable results, but training data is routinely unavailable due to some privacy and transmission limitations. Therefore, recent works focus on learning efficient networks without original training data, i.e., data-free model compression. W... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Data-Free_Knowledge_Distillation_for_Image_Super-Resolution_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Data-Free_Knowledge_Distillation_for_Image_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Data-Free_Knowledge_Distillation_for_Image_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Data-Free_Knowledge_Distillation_CVPR_2021_supplemental.pdf | null |
PluckerNet: Learn To Register 3D Line Reconstructions | Liu Liu, Hongdong Li, Haodong Yao, Ruyi Zha | Aligning two partially-overlapped 3D line reconstructions in Euclidean space is challenging, as we need to simultaneously solve line correspondences and relative pose between reconstructions. This paper proposes a neural network based method and it has three modules connected in sequence: (i) a Multilayer Perceptron (M... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_PluckerNet_Learn_To_Register_3D_Line_Reconstructions_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_PluckerNet_Learn_To_Register_3D_Line_Reconstructions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_PluckerNet_Learn_To_Register_3D_Line_Reconstructions_CVPR_2021_paper.html | CVPR 2021 | null | null |
Deep Perceptual Preprocessing for Video Coding | Aaron Chadha, Yiannis Andreopoulos | We introduce the concept of rate-aware deep perceptual preprocessing (DPP) for video encoding. DPP makes a single pass over each input frame in order to enhance its visual quality when the video is to be compressed with any codec at any bitrate. The resulting bitstreams can be decoded and displayed at the client side w... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chadha_Deep_Perceptual_Preprocessing_for_Video_Coding_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chadha_Deep_Perceptual_Preprocessing_for_Video_Coding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chadha_Deep_Perceptual_Preprocessing_for_Video_Coding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chadha_Deep_Perceptual_Preprocessing_CVPR_2021_supplemental.pdf | null |
Explaining Classifiers Using Adversarial Perturbations on the Perceptual Ball | Andrew Elliott, Stephen Law, Chris Russell | We present a simple regularization of adversarial perturbations based upon the perceptual loss. While the resulting perturbations remain imperceptible to the human eye, they differ from existing adversarial perturbations in that they are semi-sparse alterations that highlight objects and regions of interest while leavi... | https://openaccess.thecvf.com/content/CVPR2021/papers/Elliott_Explaining_Classifiers_Using_Adversarial_Perturbations_on_the_Perceptual_Ball_CVPR_2021_paper.pdf | http://arxiv.org/abs/1912.09405 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Elliott_Explaining_Classifiers_Using_Adversarial_Perturbations_on_the_Perceptual_Ball_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Elliott_Explaining_Classifiers_Using_Adversarial_Perturbations_on_the_Perceptual_Ball_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Elliott_Explaining_Classifiers_Using_CVPR_2021_supplemental.pdf | null |
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