{"id":661083,"date":"2020-05-28T15:59:06","date_gmt":"2020-05-28T22:59:06","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=661083"},"modified":"2020-06-24T14:02:24","modified_gmt":"2020-06-24T21:02:24","slug":"cvpr-2020","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/cvpr-2020\/","title":{"rendered":"Microsoft at CVPR 2020"},"content":{"rendered":"

Website:<\/strong> CVPR 2020 (opens in new tab)<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Microsoft is proud to be a Diamond Sponsor of CVPR 2020. Make sure to catch Satya Nadella\u2019s Fireside Chat at 9:00 PDT on Tuesday, June 16. Stop by our virtual booth to chat with our experts to learn more about our research and open opportunities.<\/p>\n","protected":false},"featured_media":635493,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2020-06-14","msr_enddate":"2020-06-19","msr_location":"Virtual\/Online","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":true,"msr_private_event":false,"footnotes":""},"research-area":[13556,13562,13554],"msr-region":[256048],"msr-event-type":[197941],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-661083","msr-event","type-msr-event","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-computer-vision","msr-research-area-human-computer-interaction","msr-region-global","msr-event-type-conferences","msr-locale-en_us"],"msr_about":"Website:<\/strong> CVPR 2020<\/a>","tab-content":[{"id":0,"name":"About","content":"Microsoft is proud to be a Diamond Sponsor of CVPR 2020. Make sure to catch Satya Nadella\u2019s Fireside Chat at 9:00 PDT on Tuesday, June 16. Stop by our virtual booth to chat with our experts to learn more about our research and open opportunities.\r\n\r\nhttps:\/\/youtu.be\/vgdVIeQKH-E"},{"id":1,"name":"Oral presentations","content":"

Tuesday, June 16<\/h2>\r\nOral 1.1A \u2013 3D From a Single Image and Shape-From-X (1)\r\n10:50 \u2013 10:55 PDT\r\nActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture<\/strong><\/a>\r\nSena\u00a0Kiciroglu, Helge\u00a0Rhodin,\u00a0Sudipta\u00a0Sinha<\/a>, Mathieu Salzmann, Pascal\u00a0Fua\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 1.2A \u2013 3D From Multiview and Sensors (1)\r\n12:10 \u2013 12:15 PDT\r\nTextureFusion: High-Quality Texture Acquisition for Real-Time RGB-D Scanning<\/strong><\/a>\r\nJoo\u00a0Ho Lee,\u00a0Hyunho\u00a0Ha,\u00a0Yue Dong<\/a>, Xin Tong, Min H. Kim\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 1.2C \u2013 Efficient Training and Inference\r\n12:30 \u2013 12:35 PDT\r\nTowards Efficient Model Compression via Learned Global Ranking<\/strong><\/a>\r\nTing-Wu Chin,\u00a0Ruizhou\u00a0Ding,\u00a0Cha Zhang<\/a>, Diana\u00a0Marculescu\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 1.3A - 3D From a Single Image and Shape-From-X (2); 3D From Multiview and Sensors (2)\r\n14:40 \u2013 14:45 PDT\r\nWhy Having 10,000 Parameters in Your Camera Model Is Better Than Twelve<\/strong><\/a>\r\nThomas\u00a0Sch\u00f6ps, Viktor Larsson,\u00a0Marc Pollefeys<\/a>,\u00a0Torsten\u00a0Sattler\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 1.3C \u2013 Low-Level and Physics-Based Vision\r\n14:25 \u2013 14:30 PDT\r\nBringing Old Photos Back to Life<\/strong><\/a>\r\nZiyu\u00a0Wan,\u00a0Bo Zhang<\/a>,\u00a0Dongdong Chen<\/b>,\u00a0Pan Zhang,\u00a0Dong Chen<\/a>, Jing Liao,\u00a0Fang Wen<\/a>\r\nVideo ><\/a>\r\n\r\n14:30 \u2013 14:35 PDT\r\nA Physics-based Noise Formation Model for Extreme Low-light Raw Denoising<\/strong><\/a>\r\nKaixuan\u00a0Wei, Ying Fu,\u00a0Jiaolong<\/a>\u00a0Yang,\u00a0Hua Huang\r\nVideo ><\/a>\r\n\r\n
\r\n\r\n

Wednesday, June 17<\/h2>\r\nOral 2.1A \u2013 3D From Multiview and Sensors (3)\r\n10:15 \u2013 10:20 PDT\r\nRoutedFusion: Learning Real-Time Depth Map Fusion<\/strong><\/a>\r\nSilvan\u00a0Weder,\u00a0Johannes\u00a0Sch\u00f6nberger<\/a>,\u00a0Marc Pollefeys<\/a>, Martin R. Oswald\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 2.1B \u2013 Face, Gesture, and Body Pose (1)\r\n10:00 \u2013 10:05 PDT\r\nReDA:Reinforced\u00a0Differentiable Attribute for 3D Face Reconstruction<\/strong><\/a>\r\nWenbin<\/b>\u00a0Zhu<\/b>,\u00a0HsiangTao\u00a0Wu<\/strong>,\u00a0Zeyu<\/b>\u00a0Chen<\/b>,\u00a0Noranart<\/b>\u00a0<\/b>Vesdapunt<\/b>,\u00a0Baoyuan\u00a0Wang<\/a>\r\nVideo ><\/a>\r\n\r\n10:20 \u2013 10:25 PDT\r\nFace X-ray for More General Face Forgery Detection<\/strong><\/a>\r\nLingzhi\u00a0Li,\u00a0Jianmin\u00a0Bao<\/a>,\u00a0Ting Zhang<\/a>,\u00a0Hao Yang<\/a>,\u00a0Dong Chen<\/a>,\u00a0Fang Wen<\/a>,\u00a0Baining\u00a0Guo<\/a>\r\nVideo ><\/a>\r\n\r\n10:55 \u2013 11:00 PDT\r\nAdvancing High Fidelity Identity Swapping for Forgery Detection<\/strong><\/a>\r\nLingzhi\u00a0Li,\u00a0Jianmin\u00a0Bao<\/a>,\u00a0Hao Yang<\/a>,\u00a0Dong Chen<\/a>,\u00a0Fang Wen<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 2.2B \u2013 Motion and Tracking (1)\r\n12:00 \u2013 12:05 PDT\r\nLSM: Learning Subspace Minimization for Low-level Vision<\/strong><\/a>\r\nChengzhou\u00a0Tang,\u00a0Lu Yuan<\/a>, Ping Tan\r\nVideo ><\/a>\r\n\r\n12:20 \u2013 12:25 PDT\r\nMaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask<\/strong><\/a>\r\nShengyu Zhao,\u00a0Yilun\u00a0Sheng, Yue Dong,\u00a0Eric Chang<\/a>,\u00a0Yan Xu\r\nVideo ><\/a>\r\n\r\n12:25 \u2013 12:30 PDT\r\nTracking by Instance Detection: A Meta-Learning Approach<\/strong><\/a>\r\nGuangting\u00a0Wang,\u00a0Chong Luo<\/a>,\u00a0Xiaoyan<\/b>\u00a0Sun<\/b>,\u00a0Zhiwei\u00a0Xiong,\u00a0Wenjun Zeng<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 2.1C \u2013 Image and Video Synthesis (1)\r\n10:30 \u2013 10:35 PDT\r\nCross-domain Correspondence Learning for Exemplar-based Image Translation<\/strong><\/a>\r\nPan Zhang,\u00a0Bo Zhang<\/a>,\u00a0Dong Chen<\/a>,\u00a0Lu Yuan<\/a>,\u00a0Fang Wen<\/a>\r\nVideo ><\/a>\r\n\r\n10:35 \u2013 10:40 PDT\r\nDisentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning<\/strong><\/a>\r\nYu Deng,\u00a0Jiaolong\u00a0Yang<\/a>,\u00a0Dong Chen<\/a>,\u00a0Fang Wen<\/a>,\u00a0Xin Tong<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 2.3A \u2013 Face, Gesture, and Body Pose (3); Motion and Tracking (2)\r\n14:15 \u2013 14:20 PDT\r\nRecursive Least-Squares Estimator-Aided Online Learning for Visual Tracking<\/strong><\/a>\r\nJin\u00a0Gao,\u00a0Weiming\u00a0Hu,\u00a0Yan Lu<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 2.4C \u2013 Transfer\/Low-Shot\/Semi\/Unsupervised Learning (2)\r\n16:10 \u2013 16:15 PDT\r\nHyperSTAR: Task-Aware Hyperparameters for Deep Networks<\/strong><\/a>\r\nGaurav Mittal<\/b>, Chang Liu, Nikolaos\u00a0Karianakis,\u00a0Victor Fragoso<\/a>,\u00a0Mei Chen<\/a>, Yun Fu\r\nVideo ><\/a>\r\n\r\n
\r\n\r\n

Thursday, June 18<\/h2>\r\nOral 3.1B \u2013 Video Analysis and Understanding\r\n9:05 \u2013 9:10 PDT\r\nSpatiotemporal Fusion in 3D CNNs: A Probabilistic View<\/strong><\/a>\r\nYizhou\u00a0Zhou,\u00a0Xiaoyan<\/b>\u00a0Sun<\/b>,\u00a0Chong Luo<\/a>,\u00a0Zheng-Jun\u00a0Zha,\u00a0Wenjun Zeng<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 3.1C \u2013 Vision & Language\r\n9:30 \u2013 9:35 PDT\r\nSQuINTing\u00a0at VQA Models: Introspecting VQA Models with Sub-Questions<\/strong><\/a>\r\nRamprasaath\u00a0Ramasamy\u00a0Selvaraju,\u00a0Purva\u00a0Tendulkar, Devi Parikh,\u00a0Eric Horvitz<\/a>,\u00a0Marco Ribeiro<\/a>,\u00a0Besmira Nushi<\/a>,\u00a0Ece Kamar<\/a>\r\nVideo ><\/a>\r\n\r\n9:40 \u2013 9:45\u00a0PDT\r\nSign Language Transformers: Joint End-to-end Sign Language Recognition and Translation<\/strong><\/a>\r\nNecati\u00a0Cihan\u00a0Camgoz, Simon Hadfield,\u00a0Oscar Koller<\/a>, Richard Bowden\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 3.2A \u2013 Recognition (Detection, Categorization) (2)\r\n11:25 \u2013 11:30 PDT\r\nDynamic Convolution: Attention over Convolution Kernels<\/strong><\/a>\r\nYinpeng\u00a0Chen<\/a>,\u00a0Xiyang<\/b>\u00a0Dai<\/b>,\u00a0Mengchen<\/b>\u00a0Liu<\/b>,\u00a0Dongdong Chen<\/b>,\u00a0Lu Yuan<\/a>,\u00a0Zicheng\u00a0Liu<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nOral 3.2C \u2013 Machine Learning Architectures and Formulations\r\n11:40 \u2013 11:45 PDT\r\nLocal Context Normalization: Revisiting Local Normalization<\/strong><\/a>\r\nAnthony Ortiz<\/strong>, Caleb Robinson, Md\u00a0Mahmudulla\u00a0Hassan,\u00a0Dan Morris<\/a>,\u00a0Olac\u00a0Fuentes, Christopher\u00a0Kiekintveld,\u00a0Nebojsa Jojic<\/a>\r\nVideo ><\/a>"},{"id":2,"name":"Posters","content":"

Tuesday, June 16<\/h2>\r\nPoster 1.1 \u2013 3D From a Single Image and Shape-From-X; Action and Behavior Recognition; Adversarial Learning |\u00a010:00 \u2013 12:00 PDT\r\n\r\nLeveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction\u00a0- #58<\/strong><\/a>\r\nYana Hasson,\u00a0Bugra\u00a0Tekin<\/a>,\u00a0Federica Bogo<\/a>,\u00a0Ivan Laptev,\u00a0Marc Pollefeys<\/a>, Cordelia Schmid\r\nVideo ><\/a>\r\n\r\nSelf-Supervised Human Depth Estimation From Monocular Videos\u00a0- #66<\/strong><\/a>\r\nFeitong\u00a0Tan, Hao Zhu,\u00a0Zhaopeng\u00a0Cui, Siyu Zhu,\u00a0Marc Pollefeys<\/a>,\u00a0Ping Tan\r\nVideo ><\/a>\r\n\r\nAdversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning\u00a0- #71<\/strong><\/a>\r\nTianlong Chen,\u00a0Sijia\u00a0Liu,\u00a0Shiyu\u00a0Chang,\u00a0Yu Cheng<\/b>,\u00a0Lisa\u00a0Amini,\u00a0Zhangyang\u00a0Wang\r\nVideo ><\/a>\r\n\r\nGeometry-Aware Satellite-to-Ground Image Synthesis for Urban Areas\u00a0- #87<\/strong><\/a>\r\nXiaohu\u00a0Lu,\u00a0Zuoyue\u00a0Li,\u00a0Zhaopeng\u00a0Cui, Martin R. Oswald,\u00a0Marc Pollefeys<\/a>,\u00a0Rongjun\u00a0Qin\r\nVideo ><\/a>\r\n\r\nWeakly-Supervised Action Localization by Generative Attention Modeling\u00a0- #102<\/strong><\/a>\r\nBaifeng<\/b>\u00a0Shi<\/b>,\u00a0Qi Dai<\/a>,\u00a0Yadong\u00a0Mu,\u00a0Jingdong\u00a0Wang<\/a>\r\nVideo ><\/a>\r\n\r\nSemantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition\u00a0- #112<\/strong><\/a>\r\nPengfei\u00a0Zhang,\u00a0Cuiling Lan<\/a>,\u00a0Wenjun Zeng<\/a>,\u00a0Junliang\u00a0Xing,\u00a0Jianru\u00a0Xue, Nanning Zheng\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nPoster 1.2 \u2013 3D From Multiview and Sensors; Computational Photography; Efficient Training and Inference Methods for Networks | 12:00 \u2013 14:00 PDT\r\n\r\nDIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing\u00a0- #77<\/strong><\/a>\r\nShaohui\u00a0Liu,\u00a0Yinda\u00a0Zhang,\u00a0Songyou\u00a0Peng,\u00a0Boxin\u00a0Shi,\u00a0Marc Pollefeys<\/a>,\u00a0Zhaopeng\u00a0Cui\r\nVideo ><\/a>\r\n\r\nFusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach\u00a0- #95<\/strong><\/a>\r\nZhe Zhang,\u00a0Chunyu\u00a0Wang<\/a>,\u00a0Wenhu\u00a0Qin,\u00a0Wenjun Zeng<\/a>\r\nVideo ><\/a>\r\n\r\ngDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors\u00a0- #96<\/strong><\/a>\r\nVictor Fragoso<\/a>,\u00a0Joseph\u00a0<\/b>Degol<\/b>, Gang Hua\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nPoster 1.3 \u2014 3D From a Single Image and Shape-From-X; 3D From Multiview and Sensors; Image Retrieval; Datasets and Evaluation; Low-Level and Physics-Based Vision | 14:00 \u2013 16:00 PDT\r\n\r\nStyle Normalization and Restitution for Generalizable Person Re-identification\u00a0- #69<\/strong><\/a>\r\nXin\u00a0Jin,\u00a0Cuiling Lan<\/a>,\u00a0Wenjun Zeng<\/a>,\u00a0Zhibo\u00a0Chen, Li Zhang\r\nVideo ><\/a>\r\n\r\nRelation-aware Global Attention for Person Re-identification\u00a0- #73<\/strong><\/a>\r\nZhizheng\u00a0Zhang,\u00a0Cuiling Lan<\/a>,\u00a0Wenjun Zeng<\/a>, Xin\u00a0Jin,\u00a0Zhibo\u00a0Chen\r\nVideo ><\/a>\r\n\r\nSingle Image Reflection Removal through Cascaded Refinement\u00a0- #110<\/strong><\/a>\r\nChao Li,\u00a0Yixiao\u00a0Yang,\u00a0Kun\u00a0He,\u00a0Stephen Lin<\/a>, John Hopcroft\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nPoster 1.4 \u2014 Scene Analysis and Understanding; Medical, Biological and Cell Microscopy; Transfer\/Low-Shot\/Semi\/Unsupervised Learning | 16:00 \u2013 18:00 PDT\r\n\r\nUnsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting\u00a0- #55<\/strong><\/a>\r\nDongnan\u00a0Liu,\u00a0Donghao\u00a0Zhang, Yang Song, Fan Zhang, Lauren O\u2019Donnell, Heng Huang,\u00a0Mei Chen<\/a>,\u00a0Weidong Cai\r\nVideo ><\/a>\r\n\r\nReliable Weighted Optimal Transport for Unsupervised Domain Adaptation\u00a0- #70<\/strong><\/a>\r\nRenjun\u00a0Xu,\u00a0Pelen\u00a0Liu,\u00a0Liyan\u00a0Wang, Chao Chen,\u00a0Jindong Wang<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\n

Wednesday, June 17<\/h2>\r\nPoster 2.1 - 3D From Multiview and Sensors; Face, Gesture, and Body Pose; Image and Video Synthesis | 10:00 \u2013 12:00 PDT\r\n\r\nHigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation\u00a0- #53<\/strong><\/a>\r\nBowen Cheng, Bin Xiao,\u00a0Jingdong\u00a0Wang<\/a>,\u00a0Honghui\u00a0Shi, Thomas Huang,\u00a0Lei Zhang<\/a>\r\nVideo ><\/a>\r\n\r\nLearning Texture Transformer Network for Image Super-Resolution\u00a0- #93<\/strong><\/a>\r\nFuzhi\u00a0Yang,\u00a0Huan Yang<\/a>,\u00a0Jianlong\u00a0Fu<\/a>,\u00a0Hongtao\u00a0Lu,\u00a0Baining\u00a0Guo<\/a>\r\nVideo ><\/a>\r\n\r\nDeep Shutter Unrolling Network\u00a0- #108<\/strong><\/a>\r\nPeidong\u00a0Liu,\u00a0Zhaopeng\u00a0Cui, Viktor Larsson,\u00a0Marc Pollefeys<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nPoster 2.2 \u2013 Face, Gesture, and Body Pose; Motion and Tracking; Representation Learning | 12:00 \u2013 14:00 PDT\r\n\r\nA\u00a0Transductive\u00a0Approach for Video Object Segmentation\u00a0- #84<\/strong><\/a>\r\nZhirong\u00a0Wu<\/a>,\u00a0Yizhuo\u00a0Zhang,\u00a0Houwen Peng<\/a>,\u00a0Stephen Lin<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nPoster 2.3 - Face, Gesture, and Body Pose; Motion and Tracking; Image and Video Synthesis; Nearal Generative Models; Optimization and Learning Methods | 14:00 \u2013 16:00 PDT\r\n\r\nDeep 3D Portrait from a Single Image\u00a0- #36<\/strong><\/a>\r\nSicheng\u00a0Xu,\u00a0Jiaolong\u00a0Yang<\/a>,\u00a0Dong Chen<\/a>,\u00a0Fang Wen<\/a>, Yu Deng,\u00a0Yunde\u00a0Jia,\u00a0Xin Tong<\/a>\r\nVideo ><\/a>\r\n\r\nBachGAN: High-Resolution Image Synthesis from Salient Object Layout\u00a0- #102<\/strong><\/a>\r\nYandong\u00a0Li,\u00a0Yu Cheng<\/b>,\u00a0Zhe Gan,<\/b>\u00a0Licheng\u00a0Yu,\u00a0Liqiang\u00a0Wang,\u00a0Jingjing\u00a0Liu<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\n

Thursday, June 18<\/h2>\r\nPoster 3.1 \u2014 Recognition (Detection, Categorization); Video Analysis and Understanding; Vision + Language | 9:00 \u2013 11:00 PDT\r\n\r\nRethinking Classification and Localization for Object Detection\u00a0- #49<\/strong><\/a>\r\nYue Wu,\u00a0Yinpeng\u00a0Chen<\/a>,\u00a0Lu Yuan<\/a>,\u00a0Zicheng\u00a0Liu<\/a>,\u00a0Lijuan\u00a0Wang<\/a>,\u00a0Hongzhi Li<\/a>,\u00a0Yun Fu\r\nVideo ><\/a>\r\n\r\nMemory Enhanced Global-Local Aggregation for Video Object Detection\u00a0- #64<\/strong><\/a>\r\nYihong\u00a0Chen,\u00a0Yue Cao<\/a>,\u00a0Han Hu<\/a>,\u00a0Liwei\u00a0Wang\r\nVideo ><\/a>\r\n\r\nMulti-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-\u00a0identification\u00a0- #71<\/strong><\/a>\r\nZhizheng\u00a0Zhang,\u00a0Cuiling Lan<\/a>,\u00a0Wenjun Zeng<\/a>,\u00a0Zhibo\u00a0Chen\r\nVideo ><\/a>\r\n\r\nViolin: A Large-Scale Dataset for Video-and-Language Inference\u00a0- #120<\/strong><\/a>\r\nJingzhou\u00a0Liu,\u00a0Wenhu\u00a0Chen,\u00a0Yu Cheng<\/b>,\u00a0Zhe Gan<\/b>,\u00a0Licheng\u00a0Yu,\u00a0Yiming\u00a0Yang,\u00a0Jingjing\u00a0Liu<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nPoster 3.3 \u2014 Recognition (Detection, Categorization); Segmentation, Grouping and Shape; Vision Applications and Systems; Vision & Other Modalities; Transfer\/Low-Shot\/Semi\/Unsupervised Learning | 15:00 \u2013 17:00 PDT\r\n\r\nTowards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training\u00a0- #96<\/strong><\/a>\r\nWeituo\u00a0Hao,\u00a0Chunyuan Li<\/a>,\u00a0Xiujun\u00a0Li<\/a>, Lawrence Carin Duke,\u00a0Jianfeng Gao<\/a>\r\nVideo ><\/a>\r\n\r\nMMTM: Multimodal Transfer Module for CNN Fusion\u00a0- #111<\/strong><\/a>\r\nHamid\u00a0Vaezi\u00a0Joze<\/a>,\u00a0Amirreza\u00a0Shaban, Michael\u00a0Iuzzolino,\u00a0Kazuhito\u00a0Koishida<\/a>\r\nVideo ><\/a>\r\n\r\n
\r\n\r\nPoster 3.4 - Miscellaneous | 17:00 \u2013 19:00 PDT\r\n\r\nDensity-Aware Graph for Deep Semi-Supervised Visual Recognition\u00a0- #9<\/strong><\/a>\r\nSuichan\u00a0Li, Bin Liu,\u00a0Dongdong Chen<\/b>, Qi Chu,\u00a0Lu Yuan<\/a>,\u00a0Nenghai\u00a0Yu\r\nVideo ><\/a>\r\n\r\nPFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames\u00a0- #27<\/strong><\/a>\r\nYuqi\u00a0Yang,\u00a0Shilin\u00a0Liu,\u00a0Hao Pan<\/a>,\u00a0Yang Liu<\/a>,\u00a0Xin Tong<\/a>\r\nVideo ><\/a>\r\n\r\nMetaFuse: A Pre-trained Fusion Model for Human Pose Estimation\u00a0- #38<\/strong><\/a>\r\nRongchang\u00a0Xie,\u00a0Chunyu\u00a0Wang<\/a>,\u00a0Yizhou\u00a0Wang\r\nVideo ><\/a>"},{"id":3,"name":"Workshops","content":"

June 14 | Full Day<\/h2>\r\nInternational Workshop and Challenge on Computer Vision for Physiological Measurement<\/strong><\/a>\r\nCo-Organizer:\u00a0Daniel McDuff<\/a>\r\n\r\nJoint workshop on Long Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM<\/strong><\/a>\r\nCo-Organizers:\u00a0Marc Pollefeys<\/a>,\u00a0Johannes L.\u00a0Sch\u00f6nberger<\/a>, Pablo\u00a0Speciale<\/a>\r\n\r\nThe 1st International Workshop on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture<\/strong><\/a>\r\nInvited speakers and panelists: Ranveer Chandra<\/a>, Sudipta Sinha<\/a>\r\n\r\nVizWiz\u00a0Grand Challenge: Describing Images from Blind People<\/strong><\/a>\r\nCo-Organizers:\u00a0Ed Cutrell<\/a>, Meredith Morris<\/a>\r\nInvited Speaker:\u00a0Meredith Morris<\/a>\r\nVideo ><\/a>\r\nSpeaker panel video ><\/a>\r\nPanel discussion video ><\/a>\r\n\r\nWorkshop on Fair, Data-Efficient and Trusted Computer Vision<\/strong><\/a>\r\nInvited Speaker:\u00a0Debadeepta Dey<\/a>\r\n\r\n
\r\n\r\n

June 14 | Afternoon<\/h2>\r\nWomen in Computer Vision (WiCV)<\/strong><\/a>\r\nCo-Organizer:\u00a0Azadeh<\/b>\u00a0<\/b>Mobasher<\/b>\r\n\r\n
\r\n\r\n

June 15 | Full Day<\/h2>\r\n3D Scene Understanding for Vision, Graphics, and Robotics<\/strong><\/a>\r\nInvited Speaker:\u00a0Marc Pollefeys<\/a>\r\n\r\nFourth Workshop on Computer Vision for AR\/VR<\/strong><\/a>\r\nInvited Speaker: Jamie Shotton<\/a>\r\nVideo ><\/a>\r\n\r\nNew Trends in Image Restoration and Enhancement Workshop and Challenges (NTIRE)<\/strong><\/a>\r\nProgram Committee Members:\u00a0Stephen Lin<\/a>, Wenjun Zeng<\/a>\r\n\r\n
\r\n\r\n

June 19 | Morning<\/h2>\r\nImage Matching: Local Features and Beyond<\/strong><\/a>\r\nCo-Organizer:\u00a0Johannes L.\u00a0Sch\u00f6nberger<\/a>\r\n\r\n
\r\n\r\n

June 19 | Full Day<\/h2>\r\n16th\u00a0IEEE Workshop on Perception Beyond the Visible Spectrum<\/strong><\/a>\r\nProgram Committee Member:\u00a0Katsu Ikeuchi<\/b>\r\n\r\nLearning From Unlabeled Videos<\/strong><\/a>\r\nCo-Organizer:\u00a0Yale Song<\/a>\r\n\r\nComputer Vision for Microscopy Image Analysis<\/strong><\/a>\r\nChair:\u00a0Mei Chen<\/a>\r\nProgram Committee Members:\u00a0Hao Jiang<\/b>,\u00a0Guarav<\/b>\u00a0Mittal<\/b>, Xi Yin<\/b>\r\n\r\nFirst Workshop on Deep Learning Foundations of Geometric Shape Modeling and Reconstruction<\/strong><\/a>\r\nCo-Organizer:\u00a0Yang Liu<\/a>\r\n\r\nExtreme classification in computer vision<\/strong>\r\nCo-Organizer:\u00a0Manik Varma<\/a>\r\n\r\nLanguage & Vision with applications to Video Understanding<\/strong><\/a>\r\nCo-Organizer:\u00a0Licheng<\/b>\u00a0Yu<\/b>\r\n\r\nThe 3rd Workshop and Prize Challenge: Bridging the Gap between Computational Photography and Visual Recognition (UG2+) in conjunction with IEEE CVPR 2020<\/strong><\/a>\r\nInvited Speaker:\u00a0Xi Yin<\/b>\r\n\r\nVisual Learning with Limited Labels<\/strong><\/a>\r\nAccepted Paper: ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification<\/a> Naoto Usuyama<\/a>, Natalia Larios Delgado, Amanda K. Hall, Jessica Lundin\r\nVideo ><\/a>\r\n\r\nWorkshop on Multimodal Learning<\/strong><\/a>\r\nInvited Speaker:\u00a0Andrew Fitzgibbon<\/a>"},{"id":4,"name":"Tutorials","content":"

Monday, June 15<\/h2>\r\n13:15 \u2013 17:00 PDT\r\nRecent Advances in Vision-and-Language Research<\/strong><\/a>\r\nCo-organizers: Zhe Gan<\/strong>, Yu Cheng<\/strong>, Luowei Zhou<\/strong>, Linjie Li<\/strong>, Yen-Chun Chen<\/strong>, JJ Liu<\/a>"},{"id":5,"name":"#AlchemyFriends","content":"Print your own copy<\/a> of Alchemy with Friends to play at home.\r\n\r\nShare your favorite card combinations using #AlchemyFriends on Twitter, Facebook, or Instagram. We now have three versions of the game available for you to play at home!\r\n\r\n\"Animated<\/a>\r\n
[msr-button text=\"Alchemy with Friends Original (must have this deck)\" url=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/12\/Alchemy-with-Friends-Print-at-Home.pdf\" new-window=\"true\" ]<\/div>\r\n
[msr-button text=\"Alchemy with Friends ML Expansion Pack\" url=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/06\/Alchemy-with-Friends-ML-Expansion-Pack.pdf\" new-window=\"true\" ]<\/div>\r\n
[msr-button text=\"Alchemy with Friends CV Expansion Pack\" url=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2020\/06\/Alchemy-with-Friends-CV-Expansion-Pack.pdf\" new-window=\"true\" ]<\/div>\r\n
<\/div>\r\n