paper_id stringlengths 33 138 | title stringlengths 13 147 | authors listlengths 1 17 | cvf_url stringlengths 90 195 | pdf_url stringlengths 91 196 | supp_url stringlengths 101 137 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
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Zheng_Rethinking_Semantic_Segmentation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_CVPR_2021_paper | 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"
] | 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/papers/Zheng_Rethinking_Semantic_Segmentation_From_a_Sequence-to-Sequence_Perspective_With_Transformers_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Rethinking_Semantic_Segmentation_CVPR_2021_supplemental.pdf | 2012.15840 | cvf | @InProceedings{Zheng_2021_CVPR,
author = {Zheng, Sixiao and Lu, Jiachen and Zhao, Hengshuang and Zhu, Xiatian and Luo, Zekun and Wang, Yabiao and Fu, Yanwei and Feng, Jianfeng and Xiang, Tao and Torr, Philip H.S. and Zhang, Li},
title = {Rethinking Semantic Segmentation From a Sequence-to-Sequence Perspe... | 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... |
Gu_Interpreting_Super-Resolution_Networks_With_Local_Attribution_Maps_CVPR_2021_paper | Interpreting Super-Resolution Networks With Local Attribution Maps | [
"Jinjin Gu",
"Chao Dong"
] | 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/papers/Gu_Interpreting_Super-Resolution_Networks_With_Local_Attribution_Maps_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gu_Interpreting_Super-Resolution_Networks_CVPR_2021_supplemental.pdf | 2011.11036 | cvf | @InProceedings{Gu_2021_CVPR,
author = {Gu, Jinjin and Dong, Chao},
title = {Interpreting Super-Resolution Networks With Local Attribution Maps},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | 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... |
Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper | Multi-Target Domain Adaptation With Collaborative Consistency Learning | [
"Takashi Isobe",
"Xu Jia",
"Shuaijun Chen",
"Jianzhong He",
"Yongjie Shi",
"Jianzhuang Liu",
"Huchuan Lu",
"Shengjin Wang"
] | 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/papers/Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper.pdf | null | 2106.03418 | cvf | @InProceedings{Isobe_2021_CVPR,
author = {Isobe, Takashi and Jia, Xu and Chen, Shuaijun and He, Jianzhong and Shi, Yongjie and Liu, Jianzhuang and Lu, Huchuan and Wang, Shengjin},
title = {Multi-Target Domain Adaptation With Collaborative Consistency Learning},
booktitle = {Proceedings of the IEEE/CV... | 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... |
Wang_Troubleshooting_Blind_Image_Quality_Models_in_the_Wild_CVPR_2021_paper | Troubleshooting Blind Image Quality Models in the Wild | [
"Zhihua Wang",
"Haotao Wang",
"Tianlong Chen",
"Zhangyang Wang",
"Kede Ma"
] | 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/papers/Wang_Troubleshooting_Blind_Image_Quality_Models_in_the_Wild_CVPR_2021_paper.pdf | null | 2105.06747 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Zhihua and Wang, Haotao and Chen, Tianlong and Wang, Zhangyang and Ma, Kede},
title = {Troubleshooting Blind Image Quality Models in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | 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... |
Le_Moing_Semantic_Palette_Guiding_Scene_Generation_With_Class_Proportions_CVPR_2021_paper | Semantic Palette: Guiding Scene Generation With Class Proportions | [
"Guillaume Le Moing",
"Tuan-Hung Vu",
"Himalaya Jain",
"Patrick Perez",
"Matthieu Cord"
] | 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/papers/Le_Moing_Semantic_Palette_Guiding_Scene_Generation_With_Class_Proportions_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Le_Moing_Semantic_Palette_Guiding_CVPR_2021_supplemental.pdf | 2106.01629 | cvf | @InProceedings{Le_Moing_2021_CVPR,
author = {Le Moing, Guillaume and Vu, Tuan-Hung and Jain, Himalaya and Perez, Patrick and Cord, Matthieu},
title = {Semantic Palette: Guiding Scene Generation With Class Proportions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | 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... |
Zhang_Physics-Based_Iterative_Projection_Complex_Neural_Network_for_Phase_Retrieval_in_CVPR_2021_paper | 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"
] | 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/papers/Zhang_Physics-Based_Iterative_Projection_Complex_Neural_Network_for_Phase_Retrieval_in_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Physics-Based_Iterative_Projection_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Feilong and Liu, Xianming and Guo, Cheng and Lin, Shiyi and Jiang, Junjun and Ji, Xiangyang},
title = {Physics-Based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging},
booktitle = {Proceedings of the IEEE/C... | 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... |
Yang_Causal_Attention_for_Vision-Language_Tasks_CVPR_2021_paper | Causal Attention for Vision-Language Tasks | [
"Xu Yang",
"Hanwang Zhang",
"Guojun Qi",
"Jianfei Cai"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Causal_Attention_for_Vision-Language_Tasks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Causal_Attention_for_Vision-Language_Tasks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Causal_Attention_for_CVPR_2021_supplemental.pdf | 2103.03493 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Xu and Zhang, Hanwang and Qi, Guojun and Cai, Jianfei},
title = {Causal Attention for Vision-Language Tasks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = ... | 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.... |
Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper | Scene Text Telescope: Text-Focused Scene Image Super-Resolution | [
"Jingye Chen",
"Bin Li",
"Xiangyang Xue"
] | 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/papers/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Scene_Text_Telescope_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Jingye and Li, Bin and Xue, Xiangyang},
title = {Scene Text Telescope: Text-Focused Scene Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | 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... |
Xiang_NeuTex_Neural_Texture_Mapping_for_Volumetric_Neural_Rendering_CVPR_2021_paper | NeuTex: Neural Texture Mapping for Volumetric Neural Rendering | [
"Fanbo Xiang",
"Zexiang Xu",
"Milos Hasan",
"Yannick Hold-Geoffroy",
"Kalyan Sunkavalli",
"Hao Su"
] | 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/papers/Xiang_NeuTex_Neural_Texture_Mapping_for_Volumetric_Neural_Rendering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiang_NeuTex_Neural_Texture_CVPR_2021_supplemental.zip | 2103.00762 | cvf | @InProceedings{Xiang_2021_CVPR,
author = {Xiang, Fanbo and Xu, Zexiang and Hasan, Milos and Hold-Geoffroy, Yannick and Sunkavalli, Kalyan and Su, Hao},
title = {NeuTex: Neural Texture Mapping for Volumetric Neural Rendering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | 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... |
Zhong_Improving_Calibration_for_Long-Tailed_Recognition_CVPR_2021_paper | Improving Calibration for Long-Tailed Recognition | [
"Zhisheng Zhong",
"Jiequan Cui",
"Shu Liu",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Improving_Calibration_for_Long-Tailed_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_Improving_Calibration_for_Long-Tailed_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_Improving_Calibration_for_CVPR_2021_supplemental.pdf | 2104.00466 | cvf | @InProceedings{Zhong_2021_CVPR,
author = {Zhong, Zhisheng and Cui, Jiequan and Liu, Shu and Jia, Jiaya},
title = {Improving Calibration for Long-Tailed Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | 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 ... |
Dai_Learning_Affinity-Aware_Upsampling_for_Deep_Image_Matting_CVPR_2021_paper | Learning Affinity-Aware Upsampling for Deep Image Matting | [
"Yutong Dai",
"Hao Lu",
"Chunhua Shen"
] | 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/papers/Dai_Learning_Affinity-Aware_Upsampling_for_Deep_Image_Matting_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_Learning_Affinity-Aware_Upsampling_CVPR_2021_supplemental.pdf | 2011.14288 | cvf | @InProceedings{Dai_2021_CVPR,
author = {Dai, Yutong and Lu, Hao and Shen, Chunhua},
title = {Learning Affinity-Aware Upsampling for Deep Image Matting},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {202... | 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... |
Stadler_Improving_Multiple_Pedestrian_Tracking_by_Track_Management_and_Occlusion_Handling_CVPR_2021_paper | Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling | [
"Daniel Stadler",
"Jurgen Beyerer"
] | 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/papers/Stadler_Improving_Multiple_Pedestrian_Tracking_by_Track_Management_and_Occlusion_Handling_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Stadler_2021_CVPR,
author = {Stadler, Daniel and Beyerer, Jurgen},
title = {Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | 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 ... |
Salvador_Revamping_Cross-Modal_Recipe_Retrieval_With_Hierarchical_Transformers_and_Self-Supervised_Learning_CVPR_2021_paper | Revamping Cross-Modal Recipe Retrieval With Hierarchical Transformers and Self-Supervised Learning | [
"Amaia Salvador",
"Erhan Gundogdu",
"Loris Bazzani",
"Michael Donoser"
] | 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/papers/Salvador_Revamping_Cross-Modal_Recipe_Retrieval_With_Hierarchical_Transformers_and_Self-Supervised_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Salvador_Revamping_Cross-Modal_Recipe_CVPR_2021_supplemental.pdf | 2103.13061 | cvf | @InProceedings{Salvador_2021_CVPR,
author = {Salvador, Amaia and Gundogdu, Erhan and Bazzani, Loris and Donoser, Michael},
title = {Revamping Cross-Modal Recipe Retrieval With Hierarchical Transformers and Self-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | 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... |
Huang_Geo-FARM_Geodesic_Factor_Regression_Model_for_Misaligned_Pre-Shape_Responses_in_CVPR_2021_paper | Geo-FARM: Geodesic Factor Regression Model for Misaligned Pre-Shape Responses in Statistical Shape Analysis | [
"Chao Huang",
"Anuj Srivastava",
"Rongjie Liu"
] | 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/papers/Huang_Geo-FARM_Geodesic_Factor_Regression_Model_for_Misaligned_Pre-Shape_Responses_in_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Huang_2021_CVPR,
author = {Huang, Chao and Srivastava, Anuj and Liu, Rongjie},
title = {Geo-FARM: Geodesic Factor Regression Model for Misaligned Pre-Shape Responses in Statistical Shape Analysis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | 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... |
He_MOST_A_Multi-Oriented_Scene_Text_Detector_With_Localization_Refinement_CVPR_2021_paper | 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"
] | 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/papers/He_MOST_A_Multi-Oriented_Scene_Text_Detector_With_Localization_Refinement_CVPR_2021_paper.pdf | null | 2104.01070 | cvf | @InProceedings{He_2021_CVPR,
author = {He, Minghang and Liao, Minghui and Yang, Zhibo and Zhong, Humen and Tang, Jun and Cheng, Wenqing and Yao, Cong and Wang, Yongpan and Bai, Xiang},
title = {MOST: A Multi-Oriented Scene Text Detector With Localization Refinement},
booktitle = {Proceedings of the I... | 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... |
Poulenard_A_Functional_Approach_to_Rotation_Equivariant_Non-Linearities_for_Tensor_Field_CVPR_2021_paper | A Functional Approach to Rotation Equivariant Non-Linearities for Tensor Field Networks. | [
"Adrien Poulenard",
"Leonidas J. Guibas"
] | 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/papers/Poulenard_A_Functional_Approach_to_Rotation_Equivariant_Non-Linearities_for_Tensor_Field_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Poulenard_A_Functional_Approach_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Poulenard_2021_CVPR,
author = {Poulenard, Adrien and Guibas, Leonidas J.},
title = {A Functional Approach to Rotation Equivariant Non-Linearities for Tensor Field Networks.},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | 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... |
Tang_Leveraging_Large-Scale_Weakly_Labeled_Data_for_Semi-Supervised_Mass_Detection_in_CVPR_2021_paper | 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"
] | 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/papers/Tang_Leveraging_Large-Scale_Weakly_Labeled_Data_for_Semi-Supervised_Mass_Detection_in_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Leveraging_Large-Scale_Weakly_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Tang_2021_CVPR,
author = {Tang, Yuxing and Cao, Zhenjie and Zhang, Yanbo and Yang, Zhicheng and Ji, Zongcheng and Wang, Yiwei and Han, Mei and Ma, Jie and Xiao, Jing and Chang, Peng},
title = {Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms},
... | 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... |
Dollar_Fast_and_Accurate_Model_Scaling_CVPR_2021_paper | Fast and Accurate Model Scaling | [
"Piotr Dollar",
"Mannat Singh",
"Ross Girshick"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dollar_Fast_and_Accurate_Model_Scaling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dollar_Fast_and_Accurate_Model_Scaling_CVPR_2021_paper.pdf | null | 2103.06877 | cvf | @InProceedings{Dollar_2021_CVPR,
author = {Dollar, Piotr and Singh, Mannat and Girshick, Ross},
title = {Fast and Accurate Model Scaling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | 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... |
Meuleman_Real-Time_Sphere_Sweeping_Stereo_From_Multiview_Fisheye_Images_CVPR_2021_paper | Real-Time Sphere Sweeping Stereo From Multiview Fisheye Images | [
"Andreas Meuleman",
"Hyeonjoong Jang",
"Daniel S. Jeon",
"Min H. Kim"
] | 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/papers/Meuleman_Real-Time_Sphere_Sweeping_Stereo_From_Multiview_Fisheye_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meuleman_Real-Time_Sphere_Sweeping_CVPR_2021_supplemental.zip | null | null | @InProceedings{Meuleman_2021_CVPR,
author = {Meuleman, Andreas and Jang, Hyeonjoong and Jeon, Daniel S. and Kim, Min H.},
title = {Real-Time Sphere Sweeping Stereo From Multiview Fisheye Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | 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... |
Zhou_Instant-Teaching_An_End-to-End_Semi-Supervised_Object_Detection_Framework_CVPR_2021_paper | Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework | [
"Qiang Zhou",
"Chaohui Yu",
"Zhibin Wang",
"Qi Qian",
"Hao Li"
] | 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/papers/Zhou_Instant-Teaching_An_End-to-End_Semi-Supervised_Object_Detection_Framework_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Instant-Teaching_An_End-to-End_CVPR_2021_supplemental.pdf | 2103.11402 | title_snapshot | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Qiang and Yu, Chaohui and Wang, Zhibin and Qian, Qi and Li, Hao},
title = {Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | 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... |
Lu_Taskology_Utilizing_Task_Relations_at_Scale_CVPR_2021_paper | Taskology: Utilizing Task Relations at Scale | [
"Yao Lu",
"Soren Pirk",
"Jan Dlabal",
"Anthony Brohan",
"Ankita Pasad",
"Zhao Chen",
"Vincent Casser",
"Anelia Angelova",
"Ariel Gordon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Taskology_Utilizing_Task_Relations_at_Scale_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Taskology_Utilizing_Task_Relations_at_Scale_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_Taskology_Utilizing_Task_CVPR_2021_supplemental.pdf | 2005.07289 | cvf | @InProceedings{Lu_2021_CVPR,
author = {Lu, Yao and Pirk, Soren and Dlabal, Jan and Brohan, Anthony and Pasad, Ankita and Chen, Zhao and Casser, Vincent and Angelova, Anelia and Gordon, Ariel},
title = {Taskology: Utilizing Task Relations at Scale},
booktitle = {Proceedings of the IEEE/CVF Conference ... | 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... |
Li_Progressive_Domain_Expansion_Network_for_Single_Domain_Generalization_CVPR_2021_paper | 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"
] | 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/papers/Li_Progressive_Domain_Expansion_Network_for_Single_Domain_Generalization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Progressive_Domain_Expansion_CVPR_2021_supplemental.pdf | 2103.16050 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Lei and Gao, Ke and Cao, Juan and Huang, Ziyao and Weng, Yepeng and Mi, Xiaoyue and Yu, Zhengze and Li, Xiaoya and Xia, Boyang},
title = {Progressive Domain Expansion Network for Single Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF Confere... | 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... |
Zhang_View-Guided_Point_Cloud_Completion_CVPR_2021_paper | View-Guided Point Cloud Completion | [
"Xuancheng Zhang",
"Yutong Feng",
"Siqi Li",
"Changqing Zou",
"Hai Wan",
"Xibin Zhao",
"Yandong Guo",
"Yue Gao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_View-Guided_Point_Cloud_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_View-Guided_Point_Cloud_Completion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_View-Guided_Point_Cloud_CVPR_2021_supplemental.pdf | 2104.05666 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Xuancheng and Feng, Yutong and Li, Siqi and Zou, Changqing and Wan, Hai and Zhao, Xibin and Guo, Yandong and Gao, Yue},
title = {View-Guided Point Cloud Completion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | 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... |
Xu_Generative_Hierarchical_Features_From_Synthesizing_Images_CVPR_2021_paper | Generative Hierarchical Features From Synthesizing Images | [
"Yinghao Xu",
"Yujun Shen",
"Jiapeng Zhu",
"Ceyuan Yang",
"Bolei Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Generative_Hierarchical_Features_From_Synthesizing_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Generative_Hierarchical_Features_From_Synthesizing_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Generative_Hierarchical_Features_CVPR_2021_supplemental.pdf | 2007.10379 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Yinghao and Shen, Yujun and Zhu, Jiapeng and Yang, Ceyuan and Zhou, Bolei},
title = {Generative Hierarchical Features From Synthesizing Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | 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... |
Mittal_Affect2MM_Affective_Analysis_of_Multimedia_Content_Using_Emotion_Causality_CVPR_2021_paper | Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality | [
"Trisha Mittal",
"Puneet Mathur",
"Aniket Bera",
"Dinesh Manocha"
] | 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/papers/Mittal_Affect2MM_Affective_Analysis_of_Multimedia_Content_Using_Emotion_Causality_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mittal_Affect2MM_Affective_Analysis_CVPR_2021_supplemental.pdf | 2103.06541 | cvf | @InProceedings{Mittal_2021_CVPR,
author = {Mittal, Trisha and Mathur, Puneet and Bera, Aniket and Manocha, Dinesh},
title = {Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | 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... |
Petsiuk_Black-Box_Explanation_of_Object_Detectors_via_Saliency_Maps_CVPR_2021_paper | Black-Box Explanation of Object Detectors via Saliency Maps | [
"Vitali Petsiuk",
"Rajiv Jain",
"Varun Manjunatha",
"Vlad I. Morariu",
"Ashutosh Mehra",
"Vicente Ordonez",
"Kate Saenko"
] | 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/papers/Petsiuk_Black-Box_Explanation_of_Object_Detectors_via_Saliency_Maps_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Petsiuk_Black-Box_Explanation_of_CVPR_2021_supplemental.pdf | 2006.03204 | cvf | @InProceedings{Petsiuk_2021_CVPR,
author = {Petsiuk, Vitali and Jain, Rajiv and Manjunatha, Varun and Morariu, Vlad I. and Mehra, Ashutosh and Ordonez, Vicente and Saenko, Kate},
title = {Black-Box Explanation of Object Detectors via Saliency Maps},
booktitle = {Proceedings of the IEEE/CVF Conference... | 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... |
Habibian_Skip-Convolutions_for_Efficient_Video_Processing_CVPR_2021_paper | Skip-Convolutions for Efficient Video Processing | [
"Amirhossein Habibian",
"Davide Abati",
"Taco S. Cohen",
"Babak Ehteshami Bejnordi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Habibian_Skip-Convolutions_for_Efficient_Video_Processing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Habibian_Skip-Convolutions_for_Efficient_Video_Processing_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Habibian_Skip-Convolutions_for_Efficient_CVPR_2021_supplemental.pdf | 2104.11487 | cvf | @InProceedings{Habibian_2021_CVPR,
author = {Habibian, Amirhossein and Abati, Davide and Cohen, Taco S. and Bejnordi, Babak Ehteshami},
title = {Skip-Convolutions for Efficient Video Processing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | 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... |
Lee_Looking_Into_Your_Speech_Learning_Cross-Modal_Affinity_for_Audio-Visual_Speech_CVPR_2021_paper | 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"
] | 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/papers/Lee_Looking_Into_Your_Speech_Learning_Cross-Modal_Affinity_for_Audio-Visual_Speech_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Looking_Into_Your_CVPR_2021_supplemental.pdf | 2104.02775 | cvf | @InProceedings{Lee_2021_CVPR,
author = {Lee, Jiyoung and Chung, Soo-Whan and Kim, Sunok and Kang, Hong-Goo and Sohn, Kwanghoon},
title = {Looking Into Your Speech: Learning Cross-Modal Affinity for Audio-Visual Speech Separation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | 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... |
Chan_GLEAN_Generative_Latent_Bank_for_Large-Factor_Image_Super-Resolution_CVPR_2021_paper | GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution | [
"Kelvin C.K. Chan",
"Xintao Wang",
"Xiangyu Xu",
"Jinwei Gu",
"Chen Change Loy"
] | 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/papers/Chan_GLEAN_Generative_Latent_Bank_for_Large-Factor_Image_Super-Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chan_GLEAN_Generative_Latent_CVPR_2021_supplemental.pdf | 2012.00739 | cvf | @InProceedings{Chan_2021_CVPR,
author = {Chan, Kelvin C.K. and Wang, Xintao and Xu, Xiangyu and Gu, Jinwei and Loy, Chen Change},
title = {GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | 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... |
Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper | Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective | [
"Jingwei Sun",
"Ang Li",
"Binghui Wang",
"Huanrui Yang",
"Hai Li",
"Yiran Chen"
] | 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/papers/Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Soteria_Provable_Defense_CVPR_2021_supplemental.pdf | 2012.06043 | title_judge | @InProceedings{Sun_2021_CVPR,
author = {Sun, Jingwei and Li, Ang and Wang, Binghui and Yang, Huanrui and Li, Hai and Chen, Yiran},
title = {Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | 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... |
Ke_Deep_Occlusion-Aware_Instance_Segmentation_With_Overlapping_BiLayers_CVPR_2021_paper | Deep Occlusion-Aware Instance Segmentation With Overlapping BiLayers | [
"Lei Ke",
"Yu-Wing Tai",
"Chi-Keung Tang"
] | 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/papers/Ke_Deep_Occlusion-Aware_Instance_Segmentation_With_Overlapping_BiLayers_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ke_Deep_Occlusion-Aware_Instance_CVPR_2021_supplemental.pdf | 2103.12340 | cvf | @InProceedings{Ke_2021_CVPR,
author = {Ke, Lei and Tai, Yu-Wing and Tang, Chi-Keung},
title = {Deep Occlusion-Aware Instance Segmentation With Overlapping BiLayers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | 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... |
Wimbauer_MonoRec_Semi-Supervised_Dense_Reconstruction_in_Dynamic_Environments_From_a_Single_CVPR_2021_paper | MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments From a Single Moving Camera | [
"Felix Wimbauer",
"Nan Yang",
"Lukas von Stumberg",
"Niclas Zeller",
"Daniel Cremers"
] | 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/papers/Wimbauer_MonoRec_Semi-Supervised_Dense_Reconstruction_in_Dynamic_Environments_From_a_Single_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wimbauer_MonoRec_Semi-Supervised_Dense_CVPR_2021_supplemental.pdf | 2011.11814 | cvf | @InProceedings{Wimbauer_2021_CVPR,
author = {Wimbauer, Felix and Yang, Nan and von Stumberg, Lukas and Zeller, Niclas and Cremers, Daniel},
title = {MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments From a Single Moving Camera},
booktitle = {Proceedings of the IEEE/CVF Conference ... | 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... |
Zhong_DAP_Detection-Aware_Pre-Training_With_Weak_Supervision_CVPR_2021_paper | DAP: Detection-Aware Pre-Training With Weak Supervision | [
"Yuanyi Zhong",
"Jianfeng Wang",
"Lijuan Wang",
"Jian Peng",
"Yu-Xiong Wang",
"Lei Zhang"
] | 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/papers/Zhong_DAP_Detection-Aware_Pre-Training_With_Weak_Supervision_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_DAP_Detection-Aware_Pre-Training_CVPR_2021_supplemental.pdf | 2103.16651 | cvf | @InProceedings{Zhong_2021_CVPR,
author = {Zhong, Yuanyi and Wang, Jianfeng and Wang, Lijuan and Peng, Jian and Wang, Yu-Xiong and Zhang, Lei},
title = {DAP: Detection-Aware Pre-Training With Weak Supervision},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit... | 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... |
Li_Spatial_Assembly_Networks_for_Image_Representation_Learning_CVPR_2021_paper | Spatial Assembly Networks for Image Representation Learning | [
"Yang Li",
"Shichao Kan",
"Jianhe Yuan",
"Wenming Cao",
"Zhihai He"
] | 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/papers/Li_Spatial_Assembly_Networks_for_Image_Representation_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Spatial_Assembly_Networks_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_CVPR,
author = {Li, Yang and Kan, Shichao and Yuan, Jianhe and Cao, Wenming and He, Zhihai},
title = {Spatial Assembly Networks for Image Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | 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... |
Ye_Linguistic_Structures_As_Weak_Supervision_for_Visual_Scene_Graph_Generation_CVPR_2021_paper | Linguistic Structures As Weak Supervision for Visual Scene Graph Generation | [
"Keren Ye",
"Adriana Kovashka"
] | 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/papers/Ye_Linguistic_Structures_As_Weak_Supervision_for_Visual_Scene_Graph_Generation_CVPR_2021_paper.pdf | null | 2105.13994 | cvf | @InProceedings{Ye_2021_CVPR,
author = {Ye, Keren and Kovashka, Adriana},
title = {Linguistic Structures As Weak Supervision for Visual Scene Graph Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | 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... |
Tang_SKFAC_Training_Neural_Networks_With_Faster_Kronecker-Factored_Approximate_Curvature_CVPR_2021_paper | 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"
] | 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/papers/Tang_SKFAC_Training_Neural_Networks_With_Faster_Kronecker-Factored_Approximate_Curvature_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_SKFAC_Training_Neural_CVPR_2021_supplemental.zip | null | null | @InProceedings{Tang_2021_CVPR,
author = {Tang, Zedong and Jiang, Fenlong and Gong, Maoguo and Li, Hao and Wu, Yue and Yu, Fan and Wang, Zidong and Wang, Min},
title = {SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature},
booktitle = {Proceedings of the IEEE/CVF Confe... | 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... |
Gao_Global2Local_Efficient_Structure_Search_for_Video_Action_Segmentation_CVPR_2021_paper | Global2Local: Efficient Structure Search for Video Action Segmentation | [
"Shang-Hua Gao",
"Qi Han",
"Zhong-Yu Li",
"Pai Peng",
"Liang Wang",
"Ming-Ming Cheng"
] | 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/papers/Gao_Global2Local_Efficient_Structure_Search_for_Video_Action_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gao_Global2Local_Efficient_Structure_CVPR_2021_supplemental.pdf | 2101.00910 | cvf | @InProceedings{Gao_2021_CVPR,
author = {Gao, Shang-Hua and Han, Qi and Li, Zhong-Yu and Peng, Pai and Wang, Liang and Cheng, Ming-Ming},
title = {Global2Local: Efficient Structure Search for Video Action Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | 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 ... |
Lei_Picasso_A_CUDA-Based_Library_for_Deep_Learning_Over_3D_Meshes_CVPR_2021_paper | Picasso: A CUDA-Based Library for Deep Learning Over 3D Meshes | [
"Huan Lei",
"Naveed Akhtar",
"Ajmal Mian"
] | 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/papers/Lei_Picasso_A_CUDA-Based_Library_for_Deep_Learning_Over_3D_Meshes_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lei_Picasso_A_CUDA-Based_CVPR_2021_supplemental.pdf | 2103.15076 | cvf | @InProceedings{Lei_2021_CVPR,
author = {Lei, Huan and Akhtar, Naveed and Mian, Ajmal},
title = {Picasso: A CUDA-Based Library for Deep Learning Over 3D Meshes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | 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 ... |
Wolf_DeFlow_Learning_Complex_Image_Degradations_From_Unpaired_Data_With_Conditional_CVPR_2021_paper | DeFlow: Learning Complex Image Degradations From Unpaired Data With Conditional Flows | [
"Valentin Wolf",
"Andreas Lugmayr",
"Martin Danelljan",
"Luc Van Gool",
"Radu Timofte"
] | 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/papers/Wolf_DeFlow_Learning_Complex_Image_Degradations_From_Unpaired_Data_With_Conditional_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wolf_DeFlow_Learning_Complex_CVPR_2021_supplemental.pdf | 2101.05796 | cvf | @InProceedings{Wolf_2021_CVPR,
author = {Wolf, Valentin and Lugmayr, Andreas and Danelljan, Martin and Van Gool, Luc and Timofte, Radu},
title = {DeFlow: Learning Complex Image Degradations From Unpaired Data With Conditional Flows},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | 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... |
Hong_Student-Teacher_Learning_From_Clean_Inputs_to_Noisy_Inputs_CVPR_2021_paper | Student-Teacher Learning From Clean Inputs to Noisy Inputs | [
"Guanzhe Hong",
"Zhiyuan Mao",
"Xiaojun Lin",
"Stanley H. Chan"
] | 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/papers/Hong_Student-Teacher_Learning_From_Clean_Inputs_to_Noisy_Inputs_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Student-Teacher_Learning_From_CVPR_2021_supplemental.pdf | 2103.07600 | cvf | @InProceedings{Hong_2021_CVPR,
author = {Hong, Guanzhe and Mao, Zhiyuan and Lin, Xiaojun and Chan, Stanley H.},
title = {Student-Teacher Learning From Clean Inputs to Noisy Inputs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | 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... |
Wang_AdvSim_Generating_Safety-Critical_Scenarios_for_Self-Driving_Vehicles_CVPR_2021_paper | 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"
] | 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/papers/Wang_AdvSim_Generating_Safety-Critical_Scenarios_for_Self-Driving_Vehicles_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_AdvSim_Generating_Safety-Critical_CVPR_2021_supplemental.zip | 2101.06549 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Jingkang and Pun, Ava and Tu, James and Manivasagam, Sivabalan and Sadat, Abbas and Casas, Sergio and Ren, Mengye and Urtasun, Raquel},
title = {AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles},
booktitle = {Proceedings of the IEEE... | 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... |
Kondratyuk_MoViNets_Mobile_Video_Networks_for_Efficient_Video_Recognition_CVPR_2021_paper | MoViNets: Mobile Video Networks for Efficient Video Recognition | [
"Dan Kondratyuk",
"Liangzhe Yuan",
"Yandong Li",
"Li Zhang",
"Mingxing Tan",
"Matthew Brown",
"Boqing Gong"
] | 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/papers/Kondratyuk_MoViNets_Mobile_Video_Networks_for_Efficient_Video_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kondratyuk_MoViNets_Mobile_Video_CVPR_2021_supplemental.pdf | 2103.11511 | cvf | @InProceedings{Kondratyuk_2021_CVPR,
author = {Kondratyuk, Dan and Yuan, Liangzhe and Li, Yandong and Zhang, Li and Tan, Mingxing and Brown, Matthew and Gong, Boqing},
title = {MoViNets: Mobile Video Networks for Efficient Video Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Com... | 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... |
Wang_IBRNet_Learning_Multi-View_Image-Based_Rendering_CVPR_2021_paper | 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"
] | 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/papers/Wang_IBRNet_Learning_Multi-View_Image-Based_Rendering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_IBRNet_Learning_Multi-View_CVPR_2021_supplemental.pdf | 2102.13090 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Qianqian and Wang, Zhicheng and Genova, Kyle and Srinivasan, Pratul P. and Zhou, Howard and Barron, Jonathan T. and Martin-Brualla, Ricardo and Snavely, Noah and Funkhouser, Thomas},
title = {IBRNet: Learning Multi-View Image-Based Rendering},
booktitle ... | 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... |
Reed_SelfAugment_Automatic_Augmentation_Policies_for_Self-Supervised_Learning_CVPR_2021_paper | SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning | [
"Colorado J Reed",
"Sean Metzger",
"Aravind Srinivas",
"Trevor Darrell",
"Kurt Keutzer"
] | 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/papers/Reed_SelfAugment_Automatic_Augmentation_Policies_for_Self-Supervised_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reed_SelfAugment_Automatic_Augmentation_CVPR_2021_supplemental.pdf | 2009.07724 | cvf | @InProceedings{Reed_2021_CVPR,
author = {Reed, Colorado J and Metzger, Sean and Srinivas, Aravind and Darrell, Trevor and Keutzer, Kurt},
title = {SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | 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... |
Ye_Adversarial_Invariant_Learning_CVPR_2021_paper | Adversarial Invariant Learning | [
"Nanyang Ye",
"Jingxuan Tang",
"Huayu Deng",
"Xiao-Yun Zhou",
"Qianxiao Li",
"Zhenguo Li",
"Guang-Zhong Yang",
"Zhanxing Zhu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Adversarial_Invariant_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_Adversarial_Invariant_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ye_Adversarial_Invariant_Learning_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Ye_2021_CVPR,
author = {Ye, Nanyang and Tang, Jingxuan and Deng, Huayu and Zhou, Xiao-Yun and Li, Qianxiao and Li, Zhenguo and Yang, Guang-Zhong and Zhu, Zhanxing},
title = {Adversarial Invariant Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | 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... |
Takahashi_Densely_Connected_Multi-Dilated_Convolutional_Networks_for_Dense_Prediction_Tasks_CVPR_2021_paper | Densely Connected Multi-Dilated Convolutional Networks for Dense Prediction Tasks | [
"Naoya Takahashi",
"Yuki Mitsufuji"
] | 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/papers/Takahashi_Densely_Connected_Multi-Dilated_Convolutional_Networks_for_Dense_Prediction_Tasks_CVPR_2021_paper.pdf | null | 2011.11844 | title_judge | @InProceedings{Takahashi_2021_CVPR,
author = {Takahashi, Naoya and Mitsufuji, Yuki},
title = {Densely Connected Multi-Dilated Convolutional Networks for Dense Prediction Tasks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Ju... | 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... |
Wang_Depth-Conditioned_Dynamic_Message_Propagation_for_Monocular_3D_Object_Detection_CVPR_2021_paper | 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"
] | 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/papers/Wang_Depth-Conditioned_Dynamic_Message_Propagation_for_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Depth-Conditioned_Dynamic_Message_CVPR_2021_supplemental.pdf | 2103.16470 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Li and Du, Liang and Ye, Xiaoqing and Fu, Yanwei and Guo, Guodong and Xue, Xiangyang and Feng, Jianfeng and Zhang, Li},
title = {Depth-Conditioned Dynamic Message Propagation for Monocular 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Co... | 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... |
Shen_S2-BNN_Bridging_the_Gap_Between_Self-Supervised_Real_and_1-Bit_Neural_CVPR_2021_paper | 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"
] | 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/papers/Shen_S2-BNN_Bridging_the_Gap_Between_Self-Supervised_Real_and_1-Bit_Neural_CVPR_2021_paper.pdf | null | 2102.08946 | title_snapshot | @InProceedings{Shen_2021_CVPR,
author = {Shen, Zhiqiang and Liu, Zechun and Qin, Jie and Huang, Lei and Cheng, Kwang-Ting and Savvides, Marios},
title = {S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-Bit Neural Networks via Guided Distribution Calibration},
booktitle = {Proceedings of t... | 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... |
Aleotti_Learning_Optical_Flow_From_Still_Images_CVPR_2021_paper | Learning Optical Flow From Still Images | [
"Filippo Aleotti",
"Matteo Poggi",
"Stefano Mattoccia"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Aleotti_Learning_Optical_Flow_From_Still_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Aleotti_Learning_Optical_Flow_From_Still_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Aleotti_Learning_Optical_Flow_CVPR_2021_supplemental.pdf | 2104.03965 | cvf | @InProceedings{Aleotti_2021_CVPR,
author = {Aleotti, Filippo and Poggi, Matteo and Mattoccia, Stefano},
title = {Learning Optical Flow From Still Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2... | 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... |
Liu_From_Shadow_Generation_To_Shadow_Removal_CVPR_2021_paper | From Shadow Generation To Shadow Removal | [
"Zhihao Liu",
"Hui Yin",
"Xinyi Wu",
"Zhenyao Wu",
"Yang Mi",
"Song Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_From_Shadow_Generation_To_Shadow_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_From_Shadow_Generation_To_Shadow_Removal_CVPR_2021_paper.pdf | null | 2103.12997 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Zhihao and Yin, Hui and Wu, Xinyi and Wu, Zhenyao and Mi, Yang and Wang, Song},
title = {From Shadow Generation To Shadow Removal},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | 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... |
Zhu_Face_Forgery_Detection_by_3D_Decomposition_CVPR_2021_paper | Face Forgery Detection by 3D Decomposition | [
"Xiangyu Zhu",
"Hao Wang",
"Hongyan Fei",
"Zhen Lei",
"Stan Z. Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Face_Forgery_Detection_by_3D_Decomposition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Face_Forgery_Detection_by_3D_Decomposition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_Face_Forgery_Detection_CVPR_2021_supplemental.pdf | 2011.09737 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Xiangyu and Wang, Hao and Fei, Hongyan and Lei, Zhen and Li, Stan Z.},
title = {Face Forgery Detection by 3D Decomposition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | 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... |
Zhang_Unsupervised_3D_Shape_Completion_Through_GAN_Inversion_CVPR_2021_paper | 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"
] | 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/papers/Zhang_Unsupervised_3D_Shape_Completion_Through_GAN_Inversion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Unsupervised_3D_Shape_CVPR_2021_supplemental.pdf | 2104.13366 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Junzhe and Chen, Xinyi and Cai, Zhongang and Pan, Liang and Zhao, Haiyu and Yi, Shuai and Yeo, Chai Kiat and Dai, Bo and Loy, Chen Change},
title = {Unsupervised 3D Shape Completion Through GAN Inversion},
booktitle = {Proceedings of the IEEE/CVF Confe... | 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... |
Hu_Pseudo_3D_Auto-Correlation_Network_for_Real_Image_Denoising_CVPR_2021_paper | Pseudo 3D Auto-Correlation Network for Real Image Denoising | [
"Xiaowan Hu",
"Ruijun Ma",
"Zhihong Liu",
"Yuanhao Cai",
"Xiaole Zhao",
"Yulun Zhang",
"Haoqian Wang"
] | 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/papers/Hu_Pseudo_3D_Auto-Correlation_Network_for_Real_Image_Denoising_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Hu_2021_CVPR,
author = {Hu, Xiaowan and Ma, Ruijun and Liu, Zhihong and Cai, Yuanhao and Zhao, Xiaole and Zhang, Yulun and Wang, Haoqian},
title = {Pseudo 3D Auto-Correlation Network for Real Image Denoising},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | 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... |
Gong_MaxUp_Lightweight_Adversarial_Training_With_Data_Augmentation_Improves_Neural_Network_CVPR_2021_paper | MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training | [
"Chengyue Gong",
"Tongzheng Ren",
"Mao Ye",
"Qiang Liu"
] | 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/papers/Gong_MaxUp_Lightweight_Adversarial_Training_With_Data_Augmentation_Improves_Neural_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gong_MaxUp_Lightweight_Adversarial_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Gong_2021_CVPR,
author = {Gong, Chengyue and Ren, Tongzheng and Ye, Mao and Liu, Qiang},
title = {MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | 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... |
Lee_Anti-Adversarially_Manipulated_Attributions_for_Weakly_and_Semi-Supervised_Semantic_Segmentation_CVPR_2021_paper | Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation | [
"Jungbeom Lee",
"Eunji Kim",
"Sungroh Yoon"
] | 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/papers/Lee_Anti-Adversarially_Manipulated_Attributions_for_Weakly_and_Semi-Supervised_Semantic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Anti-Adversarially_Manipulated_Attributions_CVPR_2021_supplemental.pdf | 2103.08896 | cvf | @InProceedings{Lee_2021_CVPR,
author = {Lee, Jungbeom and Kim, Eunji and Yoon, Sungroh},
title = {Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | 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 ... |
Zhang_Data-Free_Knowledge_Distillation_for_Image_Super-Resolution_CVPR_2021_paper | Data-Free Knowledge Distillation for Image Super-Resolution | [
"Yiman Zhang",
"Hanting Chen",
"Xinghao Chen",
"Yiping Deng",
"Chunjing Xu",
"Yunhe Wang"
] | 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/papers/Zhang_Data-Free_Knowledge_Distillation_for_Image_Super-Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Data-Free_Knowledge_Distillation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Yiman and Chen, Hanting and Chen, Xinghao and Deng, Yiping and Xu, Chunjing and Wang, Yunhe},
title = {Data-Free Knowledge Distillation for Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | 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... |
Liu_PluckerNet_Learn_To_Register_3D_Line_Reconstructions_CVPR_2021_paper | PluckerNet: Learn To Register 3D Line Reconstructions | [
"Liu Liu",
"Hongdong Li",
"Haodong Yao",
"Ruyi Zha"
] | 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/papers/Liu_PluckerNet_Learn_To_Register_3D_Line_Reconstructions_CVPR_2021_paper.pdf | null | 2012.01096 | title_judge | @InProceedings{Liu_2021_CVPR,
author = {Liu, Liu and Li, Hongdong and Yao, Haodong and Zha, Ruyi},
title = {PluckerNet: Learn To Register 3D Line Reconstructions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | 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... |
Chadha_Deep_Perceptual_Preprocessing_for_Video_Coding_CVPR_2021_paper | Deep Perceptual Preprocessing for Video Coding | [
"Aaron Chadha",
"Yiannis Andreopoulos"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chadha_Deep_Perceptual_Preprocessing_for_Video_Coding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chadha_Deep_Perceptual_Preprocessing_for_Video_Coding_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chadha_Deep_Perceptual_Preprocessing_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chadha_2021_CVPR,
author = {Chadha, Aaron and Andreopoulos, Yiannis},
title = {Deep Perceptual Preprocessing for Video Coding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pa... | 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... |
Elliott_Explaining_Classifiers_Using_Adversarial_Perturbations_on_the_Perceptual_Ball_CVPR_2021_paper | Explaining Classifiers Using Adversarial Perturbations on the Perceptual Ball | [
"Andrew Elliott",
"Stephen Law",
"Chris Russell"
] | 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/papers/Elliott_Explaining_Classifiers_Using_Adversarial_Perturbations_on_the_Perceptual_Ball_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Elliott_Explaining_Classifiers_Using_CVPR_2021_supplemental.pdf | 1912.09405 | cvf | @InProceedings{Elliott_2021_CVPR,
author = {Elliott, Andrew and Law, Stephen and Russell, Chris},
title = {Explaining Classifiers Using Adversarial Perturbations on the Perceptual Ball},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | 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... |
Hsu_DARCNN_Domain_Adaptive_Region-Based_Convolutional_Neural_Network_for_Unsupervised_Instance_CVPR_2021_paper | DARCNN: Domain Adaptive Region-Based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images | [
"Joy Hsu",
"Wah Chiu",
"Serena Yeung"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hsu_DARCNN_Domain_Adaptive_Region-Based_Convolutional_Neural_Network_for_Unsupervised_Instance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hsu_DARCNN_Domain_Adaptive_Region-Based_Convolutional_Neural_Network_for_Unsupervised_Instance_CVPR_2021_paper.pdf | null | 2104.01325 | cvf | @InProceedings{Hsu_2021_CVPR,
author = {Hsu, Joy and Chiu, Wah and Yeung, Serena},
title = {DARCNN: Domain Adaptive Region-Based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge. Labelled domain specific datasets for supervised tasks are often expensive to obtain, and furthermore discovery of novel distinct objects may be desi... |
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