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HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. FEATURE PYRAMID NETWORKS FOR OBJECT DETECTION Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2, Piotr Dollar´ 1, Ross Girshick1, Kaiming He1, Bharath Hariharan1, and Serge Belongie2 1Facebook AI Research (FAIR) 2Cornell University and Cornell Tech Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales. DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia LEARNING DEEP CNN DENOISER PRIOR FOR IMAGE RESTORATION Learning Deep CNN Denoiser Prior for Image Restoration Kai Zhang1,2, Wangmeng Zuo1,∗, Shuhang Gu2, Lei Zhang2 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 2Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China cskaizhang@gmail.com, wmzuo@hit.edu.cn, shuhanggu@gmail.com, cslzhang@comp.polyu.edu.hk DENSEASPP FOR SEMANTIC SEGMENTATION IN STREET SCENES DenseASPP for Semantic Segmentation in Street Scenes Maoke Yang Kun Yu Chi Zhang Zhiwei Li Kuiyuan Yang DeepMotion {maokeyang, kunyu, chizhang, zhiweili, kuiyuanyang}@deepmotion.ai HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. FEATURE PYRAMID NETWORKS FOR OBJECT DETECTION Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2, Piotr Dollar´ 1, Ross Girshick1, Kaiming He1, Bharath Hariharan1, and Serge Belongie2 1Facebook AI Research (FAIR) 2Cornell University and Cornell Tech Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales. DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia LEARNING DEEP CNN DENOISER PRIOR FOR IMAGE RESTORATION Learning Deep CNN Denoiser Prior for Image Restoration Kai Zhang1,2, Wangmeng Zuo1,∗, Shuhang Gu2, Lei Zhang2 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 2Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China cskaizhang@gmail.com, wmzuo@hit.edu.cn, shuhanggu@gmail.com, cslzhang@comp.polyu.edu.hk DENSEASPP FOR SEMANTIC SEGMENTATION IN STREET SCENES DenseASPP for Semantic Segmentation in Street Scenes Maoke Yang Kun Yu Chi Zhang Zhiwei Li Kuiyuan Yang DeepMotion {maokeyang, kunyu, chizhang, zhiweili, kuiyuanyang}@deepmotion.ai BBN: BILATERAL-BRANCH NETWORK WITH CUMULATIVE LEARNING FOR BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou1 Quan Cui1,2 Xiu-Shen Wei1∗ Zhao-Min Chen1,3 1Megvii Technology 2Waseda University 3Nanjing University Abstract Our work focuses on tackling the challenging but natu- UNSUPERVISED LEARNING OF PROBABLY SYMMETRIC DEFORMABLE 3D inspired from shape from symmetry and shape from shading. Shape from symmetry reconstructs symmet-ric objects from a single image by using the mirrored image as a virtual second view, provided that symmetric correspon- PRIOR GUIDED GAN BASED SEMANTIC INPAINTING Prior Guided GAN Based Semantic Inpainting Avisek Lahiri1*, Arnav Kumar Jain2∗†, Sanskar Agrawal1, Pabitra Mitra1, Prabir Kumar Biswas1 1Indian Institute of Technology Kharagpur, 2Microsoft Abstract Contemporary deep learning based semantic inpainting can be approached from two directions. BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
ENCODER-DECODERWITHATROUS SEPARABLE CONVOLUTION FOR 4 L.-C Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam 1x1 Conv 3x3 Conv rate 6 3x3 Conv rate 12 3x3 Conv rate 18 Image Pooling 1x1 Conv 1x1 Conv Low-Level VIDEOBERT: A JOINT MODEL FOR VIDEO AND LANGUAGE VideoBERT: A Joint Model for Video and Language Representation Learning Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, andCordelia Schmid
LEARNING EFFICIENT CONVOLUTIONAL NETWORKS THROUGH NETWORK Learning Efficient Convolutional Networks through Network Slimming Zhuang Liu1∗ Jianguo Li2 Zhiqiang Shen3 Gao Huang4 Shoumeng Yan2 Changshui Zhang1 1CSAI, TNList, Tsinghua University 2Intel Labs China 3Fudan University 4Cornell University {liuzhuangthu, zhiqiangshen0214}@gmail.com, {jianguo.li, shoumeng.yan}@intel.com, CHANNEL PRUNING FOR ACCELERATING VERY DEEP NEURAL NETWORKS Channel Pruning for Accelerating Very Deep Neural Networks Yihui He* Xi’an Jiaotong University Xi’an, 710049, China heyihui@stu.xjtu.edu.cn Xiangyu Zhang HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. FEATURE PYRAMID NETWORKS FOR OBJECT DETECTION Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2, Piotr Dollar´ 1, Ross Girshick1, Kaiming He1, Bharath Hariharan1, and Serge Belongie2 1Facebook AI Research (FAIR) 2Cornell University and Cornell Tech Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales. DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia LEARNING DEEP CNN DENOISER PRIOR FOR IMAGE RESTORATION Learning Deep CNN Denoiser Prior for Image Restoration Kai Zhang1,2, Wangmeng Zuo1,∗, Shuhang Gu2, Lei Zhang2 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 2Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China cskaizhang@gmail.com, wmzuo@hit.edu.cn, shuhanggu@gmail.com, cslzhang@comp.polyu.edu.hk DENSEASPP FOR SEMANTIC SEGMENTATION IN STREET SCENES DenseASPP for Semantic Segmentation in Street Scenes Maoke Yang Kun Yu Chi Zhang Zhiwei Li Kuiyuan Yang DeepMotion {maokeyang, kunyu, chizhang, zhiweili, kuiyuanyang}@deepmotion.ai HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. FEATURE PYRAMID NETWORKS FOR OBJECT DETECTION Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2, Piotr Dollar´ 1, Ross Girshick1, Kaiming He1, Bharath Hariharan1, and Serge Belongie2 1Facebook AI Research (FAIR) 2Cornell University and Cornell Tech Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales. DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia LEARNING DEEP CNN DENOISER PRIOR FOR IMAGE RESTORATION Learning Deep CNN Denoiser Prior for Image Restoration Kai Zhang1,2, Wangmeng Zuo1,∗, Shuhang Gu2, Lei Zhang2 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 2Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China cskaizhang@gmail.com, wmzuo@hit.edu.cn, shuhanggu@gmail.com, cslzhang@comp.polyu.edu.hk DENSEASPP FOR SEMANTIC SEGMENTATION IN STREET SCENES DenseASPP for Semantic Segmentation in Street Scenes Maoke Yang Kun Yu Chi Zhang Zhiwei Li Kuiyuan Yang DeepMotion {maokeyang, kunyu, chizhang, zhiweili, kuiyuanyang}@deepmotion.ai BBN: BILATERAL-BRANCH NETWORK WITH CUMULATIVE LEARNING FOR BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou1 Quan Cui1,2 Xiu-Shen Wei1∗ Zhao-Min Chen1,3 1Megvii Technology 2Waseda University 3Nanjing University Abstract Our work focuses on tackling the challenging but natu- UNSUPERVISED LEARNING OF PROBABLY SYMMETRIC DEFORMABLE 3D inspired from shape from symmetry and shape from shading. Shape from symmetry reconstructs symmet-ric objects from a single image by using the mirrored image as a virtual second view, provided that symmetric correspon- PRIOR GUIDED GAN BASED SEMANTIC INPAINTING Prior Guided GAN Based Semantic Inpainting Avisek Lahiri1*, Arnav Kumar Jain2∗†, Sanskar Agrawal1, Pabitra Mitra1, Prabir Kumar Biswas1 1Indian Institute of Technology Kharagpur, 2Microsoft Abstract Contemporary deep learning based semantic inpainting can be approached from two directions. BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
ENCODER-DECODERWITHATROUS SEPARABLE CONVOLUTION FOR 4 L.-C Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam 1x1 Conv 3x3 Conv rate 6 3x3 Conv rate 12 3x3 Conv rate 18 Image Pooling 1x1 Conv 1x1 Conv Low-Level VIDEOBERT: A JOINT MODEL FOR VIDEO AND LANGUAGE VideoBERT: A Joint Model for Video and Language Representation Learning Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, andCordelia Schmid
LEARNING EFFICIENT CONVOLUTIONAL NETWORKS THROUGH NETWORK Learning Efficient Convolutional Networks through Network Slimming Zhuang Liu1∗ Jianguo Li2 Zhiqiang Shen3 Gao Huang4 Shoumeng Yan2 Changshui Zhang1 1CSAI, TNList, Tsinghua University 2Intel Labs China 3Fudan University 4Cornell University {liuzhuangthu, zhiqiangshen0214}@gmail.com, {jianguo.li, shoumeng.yan}@intel.com, CHANNEL PRUNING FOR ACCELERATING VERY DEEP NEURAL NETWORKS Channel Pruning for Accelerating Very Deep Neural Networks Yihui He* Xi’an Jiaotong University Xi’an, 710049, China heyihui@stu.xjtu.edu.cn Xiangyu Zhang HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. FEATURE PYRAMID NETWORKS FOR OBJECT DETECTION Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2, Piotr Dollar´ 1, Ross Girshick1, Kaiming He1, Bharath Hariharan1, and Serge Belongie2 1Facebook AI Research (FAIR) 2Cornell University and Cornell Tech Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales. DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia LEARNING DEEP CNN DENOISER PRIOR FOR IMAGE RESTORATION Learning Deep CNN Denoiser Prior for Image Restoration Kai Zhang1,2, Wangmeng Zuo1,∗, Shuhang Gu2, Lei Zhang2 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 2Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China cskaizhang@gmail.com, wmzuo@hit.edu.cn, shuhanggu@gmail.com, cslzhang@comp.polyu.edu.hk DENSEASPP FOR SEMANTIC SEGMENTATION IN STREET SCENES DenseASPP for Semantic Segmentation in Street Scenes Maoke Yang Kun Yu Chi Zhang Zhiwei Li Kuiyuan Yang DeepMotion {maokeyang, kunyu, chizhang, zhiweili, kuiyuanyang}@deepmotion.ai HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. FEATURE PYRAMID NETWORKS FOR OBJECT DETECTION Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2, Piotr Dollar´ 1, Ross Girshick1, Kaiming He1, Bharath Hariharan1, and Serge Belongie2 1Facebook AI Research (FAIR) 2Cornell University and Cornell Tech Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales. DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia LEARNING DEEP CNN DENOISER PRIOR FOR IMAGE RESTORATION Learning Deep CNN Denoiser Prior for Image Restoration Kai Zhang1,2, Wangmeng Zuo1,∗, Shuhang Gu2, Lei Zhang2 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 2Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China cskaizhang@gmail.com, wmzuo@hit.edu.cn, shuhanggu@gmail.com, cslzhang@comp.polyu.edu.hk DENSEASPP FOR SEMANTIC SEGMENTATION IN STREET SCENES DenseASPP for Semantic Segmentation in Street Scenes Maoke Yang Kun Yu Chi Zhang Zhiwei Li Kuiyuan Yang DeepMotion {maokeyang, kunyu, chizhang, zhiweili, kuiyuanyang}@deepmotion.ai BBN: BILATERAL-BRANCH NETWORK WITH CUMULATIVE LEARNING FOR BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou1 Quan Cui1,2 Xiu-Shen Wei1∗ Zhao-Min Chen1,3 1Megvii Technology 2Waseda University 3Nanjing University Abstract Our work focuses on tackling the challenging but natu- UNSUPERVISED LEARNING OF PROBABLY SYMMETRIC DEFORMABLE 3D inspired from shape from symmetry and shape from shading. Shape from symmetry reconstructs symmet-ric objects from a single image by using the mirrored image as a virtual second view, provided that symmetric correspon- PRIOR GUIDED GAN BASED SEMANTIC INPAINTING Prior Guided GAN Based Semantic Inpainting Avisek Lahiri1*, Arnav Kumar Jain2∗†, Sanskar Agrawal1, Pabitra Mitra1, Prabir Kumar Biswas1 1Indian Institute of Technology Kharagpur, 2Microsoft Abstract Contemporary deep learning based semantic inpainting can be approached from two directions. BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
ENCODER-DECODERWITHATROUS SEPARABLE CONVOLUTION FOR 4 L.-C Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam 1x1 Conv 3x3 Conv rate 6 3x3 Conv rate 12 3x3 Conv rate 18 Image Pooling 1x1 Conv 1x1 Conv Low-Level VIDEOBERT: A JOINT MODEL FOR VIDEO AND LANGUAGE VideoBERT: A Joint Model for Video and Language Representation Learning Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, andCordelia Schmid
LEARNING EFFICIENT CONVOLUTIONAL NETWORKS THROUGH NETWORK Learning Efficient Convolutional Networks through Network Slimming Zhuang Liu1∗ Jianguo Li2 Zhiqiang Shen3 Gao Huang4 Shoumeng Yan2 Changshui Zhang1 1CSAI, TNList, Tsinghua University 2Intel Labs China 3Fudan University 4Cornell University {liuzhuangthu, zhiqiangshen0214}@gmail.com, {jianguo.li, shoumeng.yan}@intel.com, CHANNEL PRUNING FOR ACCELERATING VERY DEEP NEURAL NETWORKS Channel Pruning for Accelerating Very Deep Neural Networks Yihui He* Xi’an Jiaotong University Xi’an, 710049, China heyihui@stu.xjtu.edu.cn Xiangyu Zhang HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVF OPEN ACCESS
These research papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 open access. These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVF OPEN ACCESS
These research papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 open access. These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 open access. These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.CVF OPEN ACCESS
These papers, provided here by the Computer Vision Foundation, are the author-created versions for each conference or workshop listed below.They are being hosted here as a ICCV 2019 OPEN ACCESS REPOSITORY ICCV 2019 open access. These ICCV 2019 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CNN-GENERATED IMAGES ARE SURPRISINGLY EASY TO SPOT FOR NOW CNN-generated images are surprisingly easy to spot for now Sheng-Yu Wang1 Oliver Wang2 Richard Zhang2 Andrew Owens1,3 Alexei A. Efros1 UC Berkeley1 Adobe Research2 University of Michigan3 synthetic real ProGAN StyleGAN BigGAN CycleGAN StarGAN GauGAN CRN IMLE SITD Super-res. Deepfakes NON-LOCAL NEURAL NETWORKS Non-local Neural Networks Xiaolong Wang1,2∗ Ross Girshick2 Abhinav Gupta1 Kaiming He2 1Carnegie Mellon University 2Facebook AI Research Abstract Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. ADVERSARIAL EXAMPLES FOR SEMANTIC SEGMENTATION AND OBJECT Adversarial Examples for Semantic Segmentation and Object Detection Cihang Xie1∗, Jianyu Wang2∗, Zhishuai Zhang1∗, Yuyin Zhou1, Lingxi Xie1( ), Alan Yuille1 1Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA 2Baidu Research USA, Sunnyvale, CA 94089 USA {cihangxie306, wjyouch, zhshuai.zhang, zhouyuyiner, 198808xc, alan.l.yuille}@gmail.com CHANNEL PRUNING FOR ACCELERATING VERY DEEP NEURAL NETWORKS as follow: argmin β,W 1 2N 2 (1) Y − Xc i=1 β iX iW i ⊤ F subject tokβk 0 ≤c′ ·k F is Frobenius norm. X i is N ×k hk w matrix sliced from ith channel of input volumes X, i = 1,,c. W i is n ×k hk w filter weights sliced from ith channel of W. βis coefficient vector of length c for channel selection, and β BE YOUR OWN TEACHER: IMPROVE THE PERFORMANCE OF Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation Linfeng Zhang1 Jiebo Song3 Anni Gao3 Jingwei Chen4 Chenglong Bao2∗ Kaisheng Ma1∗ 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Yau Mathematical Sciences Center, Tsinghua University 3Institute for Interdisciplinary Information Core Technology UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO NEWS AND UPDATES 05/26 - Paper Presentation Requirements for CVPR 2021 available here 05/26 - CVPR'21 is accepting applicants for DEI registration waivers here 5/14 - The 24 hour Main Conference, Workshop and Tutorial schedules have been posted here 4/26 - All accepted papers MUST have one registered author by 5PM EDT on April 30 th.A student registration is acceptable and multiple papersHOME | ICCV 2021
ABOUT. ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. CVPR 2020 OPEN ACCESS REPOSITORY These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. BE YOUR OWN TEACHER: IMPROVE THE PERFORMANCE OF Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation Linfeng Zhang1 Jiebo Song3 Anni Gao3 Jingwei Chen4 Chenglong Bao2∗ Kaisheng Ma1∗ 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Yau Mathematical Sciences Center, Tsinghua University 3Institute for Interdisciplinary Information Core Technology UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO NEWS AND UPDATES 05/26 - Paper Presentation Requirements for CVPR 2021 available here 05/26 - CVPR'21 is accepting applicants for DEI registration waivers here 5/14 - The 24 hour Main Conference, Workshop and Tutorial schedules have been posted here 4/26 - All accepted papers MUST have one registered author by 5PM EDT on April 30 th.A student registration is acceptable and multiple papersHOME | ICCV 2021
ABOUT. ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. CVPR 2020 OPEN ACCESS REPOSITORY These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. BE YOUR OWN TEACHER: IMPROVE THE PERFORMANCE OF Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation Linfeng Zhang1 Jiebo Song3 Anni Gao3 Jingwei Chen4 Chenglong Bao2∗ Kaisheng Ma1∗ 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Yau Mathematical Sciences Center, Tsinghua University 3Institute for Interdisciplinary Information Core Technology UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance BBN: BILATERAL-BRANCH NETWORK WITH CUMULATIVE LEARNING FOR BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou1 Quan Cui1,2 Xiu-Shen Wei1∗ Zhao-Min Chen1,3 1Megvii Technology 2Waseda University 3Nanjing University Abstract Our work focuses on tackling the challenging but natu- FEATURE PYRAMID NETWORKS FOR OBJECT DETECTION Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2, Piotr Dollar´ 1, Ross Girshick1, Kaiming He1, Bharath Hariharan1, and Serge Belongie2 1Facebook AI Research (FAIR) 2Cornell University and Cornell Tech Abstract Feature pyramids are a basic component in recognition systems for detecting objects at different scales. UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com PRIOR GUIDED GAN BASED SEMANTIC INPAINTING Prior Guided GAN Based Semantic Inpainting Avisek Lahiri1*, Arnav Kumar Jain2∗†, Sanskar Agrawal1, Pabitra Mitra1, Prabir Kumar Biswas1 1Indian Institute of Technology Kharagpur, 2Microsoft Abstract Contemporary deep learning based semantic inpainting can be approached from two directions. BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia ENCODER-DECODERWITHATROUS SEPARABLE CONVOLUTION FOR 4 L.-C Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam 1x1 Conv 3x3 Conv rate 6 3x3 Conv rate 12 3x3 Conv rate 18 Image Pooling 1x1 Conv 1x1 Conv Low-Level CHANNEL PRUNING FOR ACCELERATING VERY DEEP NEURAL NETWORKS as follow: argmin β,W 1 2N 2 (1) Y − Xc i=1 β iX iW i ⊤ F subject tokβk 0 ≤c′ ·k F is Frobenius norm. X i is N ×k hk w matrix sliced from ith channel of input volumes X, i = 1,,c. W i is n ×k hk w filter weights sliced from ith channel of W. βis coefficient vector of length c for channel selection, and β LEARNING EFFICIENT CONVOLUTIONAL NETWORKS THROUGH NETWORK Learning Efficient Convolutional Networks through Network Slimming Zhuang Liu1∗ Jianguo Li2 Zhiqiang Shen3 Gao Huang4 Shoumeng Yan2 Changshui Zhang1 1CSAI, TNList, Tsinghua University 2Intel Labs China 3Fudan University 4Cornell University {liuzhuangthu, zhiqiangshen0214}@gmail.com, {jianguo.li, shoumeng.yan}@intel.com, HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. BBN: BILATERAL-BRANCH NETWORK WITH CUMULATIVE LEARNING FOR BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou1 Quan Cui1,2 Xiu-Shen Wei1∗ Zhao-Min Chen1,3 1Megvii Technology 2Waseda University 3Nanjing University Abstract Our work focuses on tackling the challenging but natu- BE YOUR OWN TEACHER: IMPROVE THE PERFORMANCE OF Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation Linfeng Zhang1 Jiebo Song3 Anni Gao3 Jingwei Chen4 Chenglong Bao2∗ Kaisheng Ma1∗ 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Yau Mathematical Sciences Center, Tsinghua University 3Institute for Interdisciplinary Information Core Technology UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. BBN: BILATERAL-BRANCH NETWORK WITH CUMULATIVE LEARNING FOR BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou1 Quan Cui1,2 Xiu-Shen Wei1∗ Zhao-Min Chen1,3 1Megvii Technology 2Waseda University 3Nanjing University Abstract Our work focuses on tackling the challenging but natu- BE YOUR OWN TEACHER: IMPROVE THE PERFORMANCE OF Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation Linfeng Zhang1 Jiebo Song3 Anni Gao3 Jingwei Chen4 Chenglong Bao2∗ Kaisheng Ma1∗ 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Yau Mathematical Sciences Center, Tsinghua University 3Institute for Interdisciplinary Information Core Technology UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
BBN: BILATERAL-BRANCH NETWORK WITH CUMULATIVE LEARNING FOR BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou1 Quan Cui1,2 Xiu-Shen Wei1∗ Zhao-Min Chen1,3 1Megvii Technology 2Waseda University 3Nanjing University Abstract Our work focuses on tackling the challenging but natu- MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance PRIOR GUIDED GAN BASED SEMANTIC INPAINTING Prior Guided GAN Based Semantic Inpainting Avisek Lahiri1*, Arnav Kumar Jain2∗†, Sanskar Agrawal1, Pabitra Mitra1, Prabir Kumar Biswas1 1Indian Institute of Technology Kharagpur, 2Microsoft Abstract Contemporary deep learning based semantic inpainting can be approached from two directions. BEYOND PART MODELS: PERSON RETRIEVAL WITH REFINED PART Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline) Yifan Sun1, Liang Zheng2, Yi Yang3, Qi Tian4, and Shengjin Wang1⋆ 1 Department of Electronic Engineering, Tsinghua University, China 2 Research School of Computer Science, Australian National University, Australia 3 Centre for Artificial Intelligence, University of Technology Sydney, Australia UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
DEEP HIGH-RESOLUTION REPRESENTATION LEARNING FOR HUMAN Deep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com ENCODER-DECODERWITHATROUS SEPARABLE CONVOLUTION FOR 4 L.-C Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam 1x1 Conv 3x3 Conv rate 6 3x3 Conv rate 12 3x3 Conv rate 18 Image Pooling 1x1 Conv 1x1 Conv Low-Level VIDEOBERT: A JOINT MODEL FOR VIDEO AND LANGUAGE VideoBERT: A Joint Model for Video and Language Representation Learning Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, andCordelia Schmid
LEARNING EFFICIENT CONVOLUTIONAL NETWORKS THROUGH NETWORK Learning Efficient Convolutional Networks through Network Slimming Zhuang Liu1∗ Jianguo Li2 Zhiqiang Shen3 Gao Huang4 Shoumeng Yan2 Changshui Zhang1 1CSAI, TNList, Tsinghua University 2Intel Labs China 3Fudan University 4Cornell University {liuzhuangthu, zhiqiangshen0214}@gmail.com, {jianguo.li, shoumeng.yan}@intel.com, CHANNEL PRUNING FOR ACCELERATING VERY DEEP NEURAL NETWORKS Channel Pruning for Accelerating Very Deep Neural Networks Yihui He* Xi’an Jiaotong University Xi’an, 710049, China heyihui@stu.xjtu.edu.cn Xiangyu Zhang HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVF OPEN ACCESS
These research papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 open access. These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. Due to the continuing impact and future unpredictability of the COVID-19 pandemic, CVPR 2021 is moving to a virtual event. This was a difficult decision, but the safety and well-being of our participants is ourutmost priority.
HOME | ICCV 2021
Statement from the General Chairs on the ICCV2021 Conference. March 10, 2021. The ICCV 2021 conference will be a virtual experience. This decision was not easily made, as we were eagerly looking forward to hosting a joyous reunion of our community. However, the ongoing effects of the COVID-19 pandemic and uncertainty surroundingvaccination
CVF OPEN ACCESS
These research papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 Open Access Repository. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6269-6277. Abstract. CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 open access. These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance CVPR 2020 OPEN ACCESS REPOSITORY CVPR 2020 open access. These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.CVF OPEN ACCESS
These papers, provided here by the Computer Vision Foundation, are the author-created versions for each conference or workshop listed below.They are being hosted here as a ICCV 2019 OPEN ACCESS REPOSITORY ICCV 2019 open access. These ICCV 2019 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CNN-GENERATED IMAGES ARE SURPRISINGLY EASY TO SPOT FOR NOW CNN-generated images are surprisingly easy to spot for now Sheng-Yu Wang1 Oliver Wang2 Richard Zhang2 Andrew Owens1,3 Alexei A. Efros1 UC Berkeley1 Adobe Research2 University of Michigan3 synthetic real ProGAN StyleGAN BigGAN CycleGAN StarGAN GauGAN CRN IMLE SITD Super-res. Deepfakes NON-LOCAL NEURAL NETWORKS Non-local Neural Networks Xiaolong Wang1,2∗ Ross Girshick2 Abhinav Gupta1 Kaiming He2 1Carnegie Mellon University 2Facebook AI Research Abstract Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. ADVERSARIAL EXAMPLES FOR SEMANTIC SEGMENTATION AND OBJECT Adversarial Examples for Semantic Segmentation and Object Detection Cihang Xie1∗, Jianyu Wang2∗, Zhishuai Zhang1∗, Yuyin Zhou1, Lingxi Xie1( ), Alan Yuille1 1Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA 2Baidu Research USA, Sunnyvale, CA 94089 USA {cihangxie306, wjyouch, zhshuai.zhang, zhouyuyiner, 198808xc, alan.l.yuille}@gmail.com CHANNEL PRUNING FOR ACCELERATING VERY DEEP NEURAL NETWORKS as follow: argmin β,W 1 2N 2 (1) Y − Xc i=1 β iX iW i ⊤ F subject tokβk 0 ≤c′ ·k F is Frobenius norm. X i is N ×k hk w matrix sliced from ith channel of input volumes X, i = 1,,c. W i is n ×k hk w filter weights sliced from ith channel of W. βis coefficient vector of length c for channel selection, and β BE YOUR OWN TEACHER: IMPROVE THE PERFORMANCE OF Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation Linfeng Zhang1 Jiebo Song3 Anni Gao3 Jingwei Chen4 Chenglong Bao2∗ Kaisheng Ma1∗ 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Yau Mathematical Sciences Center, Tsinghua University 3Institute for Interdisciplinary Information Core Technology UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO NEWS AND UPDATES 05/26 - Paper Presentation Requirements for CVPR 2021 available here 05/26 - CVPR'21 is accepting applicants for DEI registration waivers here 5/14 - The 24 hour Main Conference, Workshop and Tutorial schedules have been posted here 4/26 - All accepted papers MUST have one registered author by 5PM EDT on April 30 th.A student registration is acceptable and multiple papersHOME | ICCV 2021
ABOUT. ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.CVF OPEN ACCESS
These research papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CVPR 2020 OPEN ACCESS REPOSITORY These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CVPR 2020 OPEN ACCESS REPOSITORY These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance FOCAL LOSS FOR DENSE OBJECT DETECTION Focal Loss for Dense Object Detection Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He Piotr Doll´ar Facebook AI Research (FAIR) 0 0.20.4 0.6 0.8 1
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery RESIDUAL ATTENTION NETWORK FOR IMAGE CLASSIFICATION Residual Attention Network for Image Classification Fei Wang1, Mengqing Jiang2, Chen Qian1, Shuo Yang3, Cheng Li1, Honggang Zhang4, Xiaogang Wang3, Xiaoou Tang3 1SenseTime Group Limited, 2Tsinghua University, 3The Chinese University of Hong Kong, 4Beijing University of Posts and Telecommunications 1{wangfei, qianchen, chengli}@sensetime.com, 2jmq14@mails.tsinghua.edu.cn DENSEASPP FOR SEMANTIC SEGMENTATION IN STREET SCENES DenseASPP for Semantic Segmentation in Street Scenes Maoke Yang Kun Yu Chi Zhang Zhiwei Li Kuiyuan Yang DeepMotion {maokeyang, kunyu, chizhang, zhiweili, kuiyuanyang}@deepmotion.ai HOME | CVPR 2021SPONSORSORGANIZERSCVPR2020SUBMISSIONPROGRAMEXPO NEWS AND UPDATES 05/26 - Paper Presentation Requirements for CVPR 2021 available here 05/26 - CVPR'21 is accepting applicants for DEI registration waivers here 5/14 - The 24 hour Main Conference, Workshop and Tutorial schedules have been posted here 4/26 - All accepted papers MUST have one registered author by 5PM EDT on April 30 th.A student registration is acceptable and multiple papersHOME | ICCV 2021
ABOUT. ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.CVF OPEN ACCESS
These research papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CVPR 2020 OPEN ACCESS REPOSITORY These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CVPR 2020 OPEN ACCESS REPOSITORY These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance FOCAL LOSS FOR DENSE OBJECT DETECTION Focal Loss for Dense Object Detection Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He Piotr Doll´ar Facebook AI Research (FAIR) 0 0.20.4 0.6 0.8 1
LEAST SQUARES GENERATIVE ADVERSARIAL NETWORKS Least Squares Generative Adversarial Networks Xudong Mao1 Qing Li1 Haoran Xie2 Raymond Y.K. Lau3 Zhen Wang4 Stephen Paul Smolley5 1Department of Computer Science, City University of Hong Kong 2Department of Mathematics and Information Technology, The Education University of Hong Kong 3Department of Information Systems, City University of Hong Kong 4Center for Optical Imagery RESIDUAL ATTENTION NETWORK FOR IMAGE CLASSIFICATION Residual Attention Network for Image Classification Fei Wang1, Mengqing Jiang2, Chen Qian1, Shuo Yang3, Cheng Li1, Honggang Zhang4, Xiaogang Wang3, Xiaoou Tang3 1SenseTime Group Limited, 2Tsinghua University, 3The Chinese University of Hong Kong, 4Beijing University of Posts and Telecommunications 1{wangfei, qianchen, chengli}@sensetime.com, 2jmq14@mails.tsinghua.edu.cn DENSEASPP FOR SEMANTIC SEGMENTATION IN STREET SCENES DenseASPP for Semantic Segmentation in Street Scenes Maoke Yang Kun Yu Chi Zhang Zhiwei Li Kuiyuan Yang DeepMotion {maokeyang, kunyu, chizhang, zhiweili, kuiyuanyang}@deepmotion.ai MOMENTUM CONTRAST FOR UNSUPERVISED VISUAL REPRESENTATION MoCo is a mechanism for building dynamic dictionar-ies for contrastive learning, and can be used with various pretext tasks. In this paper, we follow a simple instance CVPR 2020 OPEN ACCESS REPOSITORY These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.CVF OPEN ACCESS
These papers, provided here by the Computer Vision Foundation, are the author-created versions for each conference or workshop listed below.They are being hosted here as a ICCV 2019 OPEN ACCESS REPOSITORY These ICCV 2019 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. CNN-GENERATED IMAGES ARE SURPRISINGLY EASY TO SPOT FOR NOW CNN-generated images are surprisingly easy to spot for now Sheng-Yu Wang1 Oliver Wang2 Richard Zhang2 Andrew Owens1,3 Alexei A. Efros1 UC Berkeley1 Adobe Research2 University of Michigan3 synthetic real ProGAN StyleGAN BigGAN CycleGAN StarGAN GauGAN CRN IMLE SITD Super-res. Deepfakes CVPR 2019 OPEN ACCESS REPOSITORY These CVPR 2019 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. NON-LOCAL NEURAL NETWORKS Non-local Neural Networks Xiaolong Wang1,2∗ Ross Girshick2 Abhinav Gupta1 Kaiming He2 1Carnegie Mellon University 2Facebook AI Research Abstract Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. ADVERSARIAL EXAMPLES FOR SEMANTIC SEGMENTATION AND OBJECT Adversarial Examples for Semantic Segmentation and Object Detection Cihang Xie1∗, Jianyu Wang2∗, Zhishuai Zhang1∗, Yuyin Zhou1, Lingxi Xie1( ), Alan Yuille1 1Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA 2Baidu Research USA, Sunnyvale, CA 94089 USA {cihangxie306, wjyouch, zhshuai.zhang, zhouyuyiner, 198808xc, alan.l.yuille}@gmail.com CHANNEL PRUNING FOR ACCELERATING VERY DEEP NEURAL NETWORKS as follow: argmin β,W 1 2N 2 (1) Y − Xc i=1 β iX iW i ⊤ F subject tokβk 0 ≤c′ ·k F is Frobenius norm. X i is N ×k hk w matrix sliced from ith channel of input volumes X, i = 1,,c. W i is n ×k hk w filter weights sliced from ith channel of W. βis coefficient vector of length c for channel selection, and β UNSUPERVISED FEATURE LEARNING VIA NON-PARAMETRIC INSTANCE 128D Unit Sphere O 1-th image 2-th image i-th image n-1 th image n-th image CNN backbone 128D 2048D 128D low dim L2 norm Non-param Softmax Memory Bank Figure 2: The pipeline of our unsupervised feature learning approach. We use a backbone CNN to encode each image as afeature
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UPCOMING CVF SPONSORED CONFERENCES __ admin __ June 24, 2019 June24, 2019
ICCV 2019 : October 27th – November 3rd,Seoul, Korea
CVPR 2020 : June 14th – 19th, Seattle,Washington
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CVPR 2019 VOTING
__ admin __ June 24, 2019 June24, 2019
Three motions and one non-binding poll were considered at the June 2019 PAMI-TC meeting held at CVPR. View the results of the vote.
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ECCV 2018 PAPERS ARE NOW AVAILABLE __ admin __ September 10, 2018January 16, 2019
By special arrangement with Springer, ECCV 2018 papers are now available on the open access archive. Visit the proceedings here.
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ICCV 2017 VOTING
__ admin __ October 31, 2017January 14, 2018
Four motions were considered at the October 2017 PAMI-TC meeting held at ICCV. View the results of the vote.
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CVPR 2017 VOTING
__ admin __ August 4, 2017January 14, 2018
Two motions and one non-binding polls was considered at the July 2017 PAMI-TC meeting held at CVPR. View the results of the vote.
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CVPR 2016 VOTING
__ admin __ July 5, 2016January 13, 2018
Five motions and two non-binding polls were considered at the June 2016 PAMI-TC meeting held at CVPR. View the results of the vote.
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ICCV 2015 VOTING
__ admin __ December 20, 2015January 13, 2018
One motion was raised at the December 2015 PAMI-TC meeting held at ICCV to select the location of ICCV 2019. View the resultsof the
vote.
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CVPR 2015 VOTING
__ admin __ September 7, 2015January 14, 2018
The CVF co-sponsored CVPR 2015 , and once again provided the community with an open access proceedings.
Five motions were raised at the PAMI-TC meeting, as well as two non-binding polls related to professional memberships. View theresults
of
the vote.
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CVPR 2014 VOTING
__ admin __ June 28, 2014January 13, 2018
CVPR 2014 , the second edition of CVPR sponsored by the CVF, has concluded. As is typical for our community, the organizers did afantastic job.
Four motions were
raised at the PAMI-TC meeting, as well as one non-binding poll related to potential locations for CVPR 2017. View the resultsof the
vote.
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ICCV 2013 AWARDS AND VOTING __ admin __ December 13, 2013January 13, 2018
ICCV 2013 , the first international conference co-sponsored by the CVF, was a resounding success. We thank the organizing committee for their tremendous effort.View the slides
from the awards ceremony, including the Marr Prize, PAMI-TC Helmholtz Test-of-Time Award, PAMI-TC Mark Everingham Prize, PAMI-TC Distinguished Researcher Award and Best Reviewers. Jan Koenderink was awarded the Azriel Rosenfeld Lifetime Achievement Award. View his acceptance speech here .Three motions
were raised at the PAMI-TC meeting. View the resultsof the vote.
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