Presentation Schedule
All times are in Central time zone
Date: Wednesday, June 22, 2022 2:30PM – 5:00PM
| Session Title | Poster ID | Title | Authors |
| 3D From Multi-View & Sensors | 46b | AirObject: A Temporally Evolving Graph Embedding for Object Identification |
Nikhil Varma Keetha; Chen Wang; Yuheng Qiu; Kuan Xu; Sebastian Scherer |
| 47b | Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection From Point Clouds |
Chenhang He; Ruihuang Li; Shuai Li; Lei Zhang |
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48b | SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud |
Chuandong Liu; Chenqiang Gao; Fangcen Liu; Jiang Liu; Deyu Meng; Xinbo Gao |
| 49b | Back to Reality: Weakly-Supervised 3D Object Detection With Shape-Guided Label Enhancement |
Xiuwei Xu; Yifan Wang; Yu Zheng; Yongming Rao; Jie Zhou; Jiwen Lu |
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| 50b | VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention |
Shengheng Deng; Zhihao Liang; Lin Sun; Kui Jia |
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| 51b | Embracing Single Stride 3D Object Detector With Sparse Transformer |
Lue Fan; Ziqi Pang; Tianyuan Zhang; Yu-Xiong Wang; Hang Zhao; Feng Wang; Naiyan Wang; Zhaoxiang Zhang |
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| 52b | Point Density-Aware Voxels for LiDAR 3D Object Detection |
Jordan S. K. Hu; Tianshu Kuai; Steven L. Waslander |
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| 53b | Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation |
Yuenan Hou; Xinge Zhu; Yuexin Ma; Chen Change Loy; Yikang Li |
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| 54b | Contrastive Boundary Learning for Point Cloud Segmentation |
Liyao Tang; Yibing Zhan; Zhe Chen; Baosheng Yu; Dacheng Tao |
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| 55b | Stratified Transformer for 3D Point Cloud Segmentation |
Xin Lai; Jianhui Liu; Li Jiang; Liwei Wang; Hengshuang Zhao; Shu Liu; Xiaojuan Qi; Jiaya Jia |
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| 56b | No Pain, Big Gain: Classify Dynamic Point Cloud Sequences With Static Models by Fitting Feature-Level Space-Time Surfaces |
Jia-Xing Zhong; Kaichen Zhou; Qingyong Hu; Bing Wang; Niki Trigoni; Andrew Markham |
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| 57b | Point2Seq: Detecting 3D Objects As Sequences |
Yujing Xue; Jiageng Mao; Minzhe Niu; Hang Xu; Michael Bi Mi; Wei Zhang; Xiaogang Wang; Xinchao Wang |
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| 58b | PTTR: Relational 3D Point Cloud Object Tracking With Transformer |
Changqing Zhou; Zhipeng Luo; Yueru Luo; Tianrui Liu; Liang Pan; Zhongang Cai; Haiyu Zhao; Shijian Lu |
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| 59b | A Unified Query-Based Paradigm for Point Cloud Understanding |
Zetong Yang; Li Jiang; Yanan Sun; Bernt Schiele; Jiaya Jia |
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| 60b | PointCLIP: Point Cloud Understanding by CLIP |
Renrui Zhang; Ziyu Guo; Wei Zhang; Kunchang Li; Xupeng Miao; Bin Cui; Yu Qiao; Peng Gao; Hongsheng Li |
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| 61b | X-Trans2Cap: Cross-Modal Knowledge Transfer Using Transformer for 3D Dense Captioning |
Zhihao Yuan; Xu Yan; Yinghong Liao; Yao Guo; Guanbin Li; Shuguang Cui; Zhen Li |
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| 62b | MVS2D: Efficient Multi-View Stereo via Attention-Driven 2D Convolutions |
Zhenpei Yang; Zhile Ren; Qi Shan; Qixing Huang |
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| 63b | TransMVSNet: Global Context-Aware Multi-View Stereo Network With Transformers |
Yikang Ding; Wentao Yuan; Qingtian Zhu; Haotian Zhang; Xiangyue Liu; Yuanjiang Wang; Xiao Liu |
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| 64b | RayMVSNet: Learning Ray-Based 1D Implicit Fields for Accurate Multi-View Stereo |
Junhua Xi; Yifei Shi; Yijie Wang; Yulan Guo; Kai Xu |
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| 65b | IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo |
Fangjinhua Wang; Silvano Galliani; Christoph Vogel; Marc Pollefeys |
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| 66b | PSMNet: Position-Aware Stereo Merging Network for Room Layout Estimation |
Haiyan Wang; Will Hutchcroft; Yuguang Li; Zhiqiang Wan; Ivaylo Boyadzhiev; Yingli Tian; Sing Bing Kang |
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| 67b | Non-Parametric Depth Distribution Modelling Based Depth Inference for Multi-View Stereo | Jiayu Yang; Jose M. Alvarez; Miaomiao Liu | |
| 68b | Differentiable Stereopsis: Meshes From Multiple Views Using Differentiable Rendering |
Shubham Goel; Georgia Gkioxari; Jitendra Malik |
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| 69b | Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation |
Rui Peng; Rongjie Wang; Zhenyu Wang; Yawen Lai; Ronggang Wang |
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| 70b | Efficient Multi-View Stereo by Iterative Dynamic Cost Volume | Shaoqian Wang; Bo Li; Yuchao Dai | |
| 71b | PlaneMVS: 3D Plane Reconstruction From Multi-View Stereo |
Jiachen Liu; Pan Ji; Nitin Bansal; Changjiang Cai; Qingan Yan; Xiaolei Huang; Yi Xu |
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| 72b | Discrete Time Convolution for Fast Event-Based Stereo |
Kaixuan Zhang; Kaiwei Che; Jianguo Zhang; Jie Cheng; Ziyang Zhang; Qinghai Guo; Luziwei Leng |
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| 73b | Stereo Magnification With Multi-Layer Images |
Taras Khakhulin; Denis Korzhenkov; Pavel Solovev; Gleb Sterkin; Andrei-Timotei Ardelean; Victor Lempitsky |
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| Motion & Tracking | 74b | TransforMatcher: Match-to-Match Attention for Semantic Correspondence | Seungwook Kim; Juhong Min; Minsu Cho |
| 75b | Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences |
Prune Truong; Martin Danelljan; Fisher Yu; Luc Van Gool |
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| 76b | Locality-Aware Inter– and Intra-Video Reconstruction for Self-Supervised Correspondence Learning |
Liulei Li; Tianfei Zhou; Wenguan Wang; Lu Yang; Jianwu Li; Yi Yang |
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| 77b | Transforming Model Prediction for Tracking |
Christoph Mayer; Martin Danelljan; Goutam Bhat; Matthieu Paul; Danda Pani Paudel; Fisher Yu; Luc Van Gool |
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| 78b | Ranking-Based Siamese Visual Tracking | Feng Tang; Qiang Ling | |
| 79b | Correlation-Aware Deep Tracking |
Fei Xie; Chunyu Wang; Guangting Wang; Yue Cao; Wankou Yang; Wenjun Zeng |
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| 80b | Global Tracking via Ensemble of Local Trackers |
Zikun Zhou; Jianqiu Chen; Wenjie Pei; Kaige Mao; Hongpeng Wang; Zhenyu He |
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| 81b | Global Tracking Transformers |
Xingyi Zhou; Tianwei Yin; Vladlen Koltun; Philipp Krähenbühl |
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| 82b | Unified Transformer Tracker for Object Tracking |
Fan Ma; Mike Zheng Shou; Linchao Zhu; Haoqi Fan; Yilei Xu; Yi Yang; Zhicheng Yan |
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| 83b | Transformer Tracking With Cyclic Shifting Window Attention |
Zikai Song; Junqing Yu; Yi-Ping Phoebe Chen; Wei Yang |
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| 84b | Spiking Transformers for Event-Based Single Object Tracking |
Jiqing Zhang; Bo Dong; Haiwei Zhang; Jianchuan Ding; Felix Heide; Baocai Yin; Xin Yang |
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| 85b | Adiabatic Quantum Computing for Multi Object Tracking |
Jan-Nico Zaech; Alexander Liniger; Martin Danelljan; Dengxin Dai; Luc Van Gool |
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| 86b | HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction |
Zikang Zhou; Luyao Ye; Jianping Wang; Kui Wu; Kejie Lu |
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| 87b | Towards Discriminative Representation: Multi-View Trajectory Contrastive Learning for Online Multi-Object Tracking | En Yu; Zhuoling Li; Shoudong Han | |
| 88b | TrackFormer: Multi-Object Tracking With Transformers |
Tim Meinhardt; Alexander Kirillov; Laura Leal-Taixé; Christoph Feichtenhofer |
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| 89b | Learning of Global Objective for Network Flow in Multi-Object Tracking | Shuai Li; Yu Kong; Hamid Rezatofighi | |
| 90b | LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking |
Duy M. H. Nguyen; Roberto Henschel; Bodo Rosenhahn; Daniel Sonntag; Paul Swoboda |
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| 91b | Multi-Object Tracking Meets Moving UAV | Shuai Liu; Xin Li; Huchuan Lu; You He | |
| 92b | Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline |
Pengyu Zhang; Jie Zhao; Dong Wang; Huchuan Lu; Xiang Ruan |
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| 93b | Unsupervised Domain Adaptation for Nighttime Aerial Tracking |
Junjie Ye; Changhong Fu; Guangze Zheng; Danda Pani Paudel; Guang Chen |
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| 94b | Learning Optical Flow With Kernel Patch Attention | Ao Luo; Fan Yang; Xin Li; Shuaicheng Liu | |
| 95b | Towards Understanding Adversarial Robustness of Optical Flow Networks |
Simon Schrodi; Tonmoy Saikia; Thomas Brox |
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| 96b | DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow |
Zihua Zheng; Ni Nie; Zhi Ling; Pengfei Xiong; Jiangyu Liu; Hao Wang; Jiankun Li |
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| Computer Vision Theory | 97b | On the Instability of Relative Pose Estimation and RANSAC’s Role | Hongyi Fan; Joe Kileel; Benjamin Kimia |
| 98b | Bootstrapping ViTs: Towards Liberating Vision Transformers From Pre-Training |
Haofei Zhang; Jiarui Duan; Mengqi Xue; Jie Song; Li Sun; Mingli Song |
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| 99b | Global Sensing and Measurements Reuse for Image Compressed Sensing | Zi-En Fan; Feng Lian; Jia-Ni Quan | |
| 100b | Maximum Consensus by Weighted Influences of Monotone Boolean Functions |
Erchuan Zhang; David Suter; Ruwan Tennakoon; Tat-Jun Chin; Alireza Bab-Hadiashar; Giang Truong; Syed Zulqarnain Gilani |
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| 101b | MS2DG-Net: Progressive Correspondence Learning via Multiple Sparse Semantics Dynamic Graph |
Luanyuan Dai; Yizhang Liu; Jiayi Ma; Lifang Wei; Taotao Lai; Changcai Yang; Riqing Chen |
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| 102b | Styleformer: Transformer Based Generative Adversarial Networks With Style Vector | Jeeseung Park; Younggeun Kim | |
| 103b | Scanline Homographies for Rolling-Shutter Plane Absolute Pose | Fang Bai; Agniva Sengupta; Adrien Bartoli | |
| Transfer / Low-Shot / Long-Tail Learning | 104b | Generating Representative Samples for Few-Shot Classification | Jingyi Xu; Hieu Le |
| 105b | Matching Feature Sets for Few-Shot Image Classification |
Arman Afrasiyabi; Hugo Larochelle; Jean-François Lalonde; Christian Gagné |
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| 106b | Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations |
Junhao Dong; Yuan Wang; Jian-Huang Lai; Xiaohua Xie |
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| 107b | Sylph: A Hypernetwork Framework for Incremental Few-Shot Object Detection | Li Yin; Juan M. Perez-Rua; Kevin J. Liang | |
| 108b | Forward Compatible Few-Shot Class-Incremental Learning |
Da-Wei Zhou; Fu-Yun Wang; Han-Jia Ye; Liang Ma; Shiliang Pu; De-Chuan Zhan |
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| 109b | Constrained Few-Shot Class-Incremental Learning |
Michael Hersche; Geethan Karunaratne; Giovanni Cherubini; Luca Benini; Abu Sebastian; Abbas Rahimi |
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| 110b | Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference |
Shell Xu Hu; Da Li; Jan Stühmer; Minyoung Kim; Timothy M. Hospedales |
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| 111b | EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot Learning | Hao Zhu; Piotr Koniusz | |
| 112b | Few-Shot Learning With Noisy Labels |
Kevin J. Liang; Samrudhdhi B. Rangrej; Vladan Petrovic; Tal Hassner |
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| 113b | Ranking Distance Calibration for Cross-Domain Few-Shot Learning |
Pan Li; Shaogang Gong; Chengjie Wang; Yanwei Fu |
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| 114b | Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning |
Moslem Yazdanpanah; Aamer Abdul Rahman; Muawiz Chaudhary; Christian Desrosiers; Mohammad Havaei; Eugene Belilovsky; Samira Ebrahimi Kahou |
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| 115b | Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-Shot Learning |
Yangji He; Weihan Liang; Dongyang Zhao; Hong-Yu Zhou; Weifeng Ge; Yizhou Yu; Wenqiang Zhang |
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| 116b | Learning To Memorize Feature Hallucination for One-Shot Image Generation |
Yu Xie; Yanwei Fu; Ying Tai; Yun Cao; Junwei Zhu; Chengjie Wang |
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| 117b | A Closer Look at Few-Shot Image Generation |
Yunqing Zhao; Henghui Ding; Houjing Huang; Ngai-Man Cheung |
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| 118b | Motion-Modulated Temporal Fragment Alignment Network for Few-Shot Action Recognition |
Jiamin Wu; Tianzhu Zhang; Zhe Zhang; Feng Wu; Yongdong Zhang |
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| 119b | Knowledge Distillation As Efficient Pre-Training: Faster Convergence, Higher Data-Efficiency, and Better Transferability |
Ruifei He; Shuyang Sun; Jihan Yang; Song Bai; Xiaojuan Qi |
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| 120b | Transferability Estimation Using Bhattacharyya Class Separability |
Michal Pándy; Andrea Agostinelli; Jasper Uijlings; Vittorio Ferrari; Thomas Mensink |
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| 121b | Revisiting the Transferability of Supervised Pretraining: An MLP Perspective |
Yizhou Wang; Shixiang Tang; Feng Zhu; Lei Bai; Rui Zhao; Donglian Qi; Wanli Ouyang |
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| 122b | Task2Sim: Towards Effective Pre-Training and Transfer From Synthetic Data |
Samarth Mishra; Rameswar Panda; Cheng Perng Phoo; Chun-Fu (Richard) Chen; Leonid Karlinsky; Kate Saenko; Venkatesh Saligrama; Rogerio S. Feris |
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| 123b | Which Model To Transfer? Finding the Needle in the Growing Haystack |
Cedric Renggli; André Susano Pinto; Luka Rimanic; Joan Puigcerver; Carlos Riquelme; Ce Zhang; Mario Lučić |
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| 124b | Does Robustness on ImageNet Transfer to Downstream Tasks? | Yutaro Yamada; Mayu Otani | |
| 125b | What Makes Transfer Learning Work for Medical Images: Feature Reuse & Other Factors |
Christos Matsoukas; Johan Fredin Haslum; Moein Sorkhei; Magnus Söderberg; Kevin Smith |
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| 126b | OW-DETR: Open-World Detection Transformer |
Akshita Gupta; Sanath Narayan; K J Joseph; Salman Khan; Fahad Shahbaz Khan; Mubarak Shah |
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| 127b | Unseen Classes at a Later Time? No Problem |
Hari Chandana Kuchibhotla; Sumitra S Malagi; Shivam Chandhok; Vineeth N Balasubramanian |
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| 128b | Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism |
Binbin Yang; Xinchi Deng; Han Shi; Changlin Li; Gengwei Zhang; Hang Xu; Shen Zhao; Liang Lin; Xiaodan Liang |
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| 129b | On Generalizing Beyond Domains in Cross-Domain Continual Learning |
Christian Simon; Masoud Faraki; Yi-Hsuan Tsai; Xiang Yu; Samuel Schulter; Yumin Suh; Mehrtash Harandi; Manmohan Chandraker |
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| 130b | Online Continual Learning on a Contaminated Data Stream With Blurry Task Boundaries |
Jihwan Bang; Hyunseo Koh; Seulki Park; Hwanjun Song; Jung-Woo Ha; Jonghyun Choi |
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| 131b | DyTox: Transformers for Continual Learning With DYnamic TOken eXpansion |
Arthur Douillard; Alexandre Ramé; Guillaume Couairon; Matthieu Cord |
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| 132b | Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning |
Kai Zhu; Wei Zhai; Yang Cao; Jiebo Luo; Zheng-Jun Zha |
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| 133b | En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning |
Xia Kong; Zuodong Gao; Xiaofan Li; Ming Hong; Jun Liu; Chengjie Wang; Yuan Xie; Yanyun Qu |
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| 134b | VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning |
Wenjia Xu; Yongqin Xian; Jiuniu Wang; Bernt Schiele; Zeynep Akata |
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| 135b | Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning |
Xiangyu Li; Xu Yang; Kun Wei; Cheng Deng; Muli Yang |
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| 136b | KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning |
Shyamgopal Karthik; Massimiliano Mancini; Zeynep Akata |
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| 137b | Non-Generative Generalized Zero-Shot Learning via Task-Correlated Disentanglement and Controllable Samples Synthesis |
Yaogong Feng; Xiaowen Huang; Pengbo Yang; Jian Yu; Jitao Sang |
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| 138b | WALT: Watch and Learn 2D Amodal Representation From Time-Lapse Imagery |
N. Dinesh Reddy; Robert Tamburo; Srinivasa G. Narasimhan |
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| Recognition: Detection, Categorization, Retrieval | 139b | Omni-DETR: Omni-Supervised Object Detection With Transformers |
Pei Wang; Zhaowei Cai; Hao Yang; Gurumurthy Swaminathan; Nuno Vasconcelos; Bernt Schiele; Stefano Soatto |
| 140b | DESTR: Object Detection With Split Transformer | Liqiang He; Sinisa Todorovic | |
| 141b | A Dual Weighting Label Assignment Scheme for Object Detection |
Shuai Li; Chenhang He; Ruihuang Li; Lei Zhang |
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| 142b | Entropy-Based Active Learning for Object Detection With Progressive Diversity Constraint | Jiaxi Wu; Jiaxin Chen; Di Huang | |
| 143b | Localization Distillation for Dense Object Detection |
Zhaohui Zheng; Rongguang Ye; Ping Wang; Dongwei Ren; Wangmeng Zuo; Qibin Hou; Ming-Ming Cheng |
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| 144b | Group R-CNN for Weakly Semi-Supervised Object Detection With Points |
Shilong Zhang; Zhuoran Yu; Liyang Liu; Xinjiang Wang; Aojun Zhou; Kai Chen |
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| 145b | Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation | Tao Feng; Mang Wang; Hangjie Yuan | |
| 146b | CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping |
Jilan Xu; Junlin Hou; Yuejie Zhang; Rui Feng; Rui-Wei Zhao; Tao Zhang; Xuequan Lu; Shang Gao |
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| 147b | One Loss for Quantization: Deep Hashing With Discrete Wasserstein Distributional Matching | Khoa D. Doan; Peng Yang; Ping Li | |
| 148b | PSTR: End-to-End One-Step Person Search With Transformers |
Jiale Cao; Yanwei Pang; Rao Muhammad Anwer; Hisham Cholakkal; Jin Xie; Mubarak Shah; Fahad Shahbaz Khan |
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| 149b | Protecting Celebrities From DeepFake With Identity Consistency Transformer |
Xiaoyi Dong; Jianmin Bao; Dongdong Chen; Ting Zhang; Weiming Zhang; Nenghai Yu; Dong Chen; Fang Wen; Baining Guo |
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| 150b | MDAN: Multi-Level Dependent Attention Network for Visual Emotion Analysis |
Liwen Xu; Zhengtao Wang; Bin Wu; Simon Lui |
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| 151b | Contextual Similarity Distillation for Asymmetric Image Retrieval |
Hui Wu; Min Wang; Wengang Zhou; Houqiang Li; Qi Tian |
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| 152b | Improving Visual Grounding With Visual-Linguistic Verification and Iterative Reasoning |
Li Yang; Yan Xu; Chunfeng Yuan; Wei Liu; Bing Li; Weiming Hu |
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| 153b | MPC: Multi-View Probabilistic Clustering |
Junjie Liu; Junlong Liu; Shaotian Yan; Rongxin Jiang; Xiang Tian; Boxuan Gu; Yaowu Chen; Chen Shen; Jianqiang Huang |
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| 154b | Text Spotting Transformers |
Xiang Zhang; Yongwen Su; Subarna Tripathi; Zhuowen Tu |
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| 155b | Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting |
Min Shi; Hao Lu; Chen Feng; Chengxin Liu; Zhiguo Cao |
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| 156b | Reflection and Rotation Symmetry Detection via Equivariant Learning |
Ahyun Seo; Byungjin Kim; Suha Kwak; Minsu Cho |
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| 157b | Learning To Imagine: Diversify Memory for Incremental Learning Using Unlabeled Data | Yu-Ming Tang; Yi-Xing Peng; Wei-Shi Zheng | |
| 158b | A Simple Episodic Linear Probe Improves Visual Recognition in the Wild |
Yuanzhi Liang; Linchao Zhu; Xiaohan Wang; Yi Yang |
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| 159b | Cross Domain Object Detection by Target-Perceived Dual Branch Distillation |
Mengzhe He; Yali Wang; Jiaxi Wu; Yiru Wang; Hanqing Li; Bo Li; Weihao Gan; Wei Wu; Yu Qiao |
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| 160b | Multi-Granularity Alignment Domain Adaptation for Object Detection |
Wenzhang Zhou; Dawei Du; Libo Zhang; Tiejian Luo; Yanjun Wu |
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| 161b | Expanding Low-Density Latent Regions for Open-Set Object Detection |
Jiaming Han; Yuqiang Ren; Jian Ding; Xingjia Pan; Ke Yan; Gui-Song Xia |
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| 162b | Class-Incremental Learning With Strong Pre-Trained Models |
Tz-Ying Wu; Gurumurthy Swaminathan; Zhizhong Li; Avinash Ravichandran; Nuno Vasconcelos; Rahul Bhotika; Stefano Soatto |
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| 163b | ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cues |
Hengcan Shi; Munawar Hayat; Yicheng Wu; Jianfei Cai |
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| Self-, Semi-, Meta-, & Unsupervised Learning | 164b | Self-Supervised Models Are Continual Learners |
Enrico Fini; Victor G. Turrisi da Costa; Xavier Alameda-Pineda; Elisa Ricci; Karteek Alahari; Julien Mairal |
| 165b | The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization |
Zeyi Huang; Haohan Wang; Dong Huang; Yong Jae Lee; Eric P. Xing |
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| 166b | Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning | Matthew Gwilliam; Abhinav Shrivastava | |
| 167b | SimMIM: A Simple Framework for Masked Image Modeling |
Zhenda Xie; Zheng Zhang; Yue Cao; Yutong Lin; Jianmin Bao; Zhuliang Yao; Qi Dai; Han Hu |
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| 168b | Semantic-Aware Auto-Encoders for Self-Supervised Representation Learning |
Guangrun Wang; Yansong Tang; Liang Lin; Philip H.S. Torr |
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| 169b | UniCon: Combating Label Noise Through Uniform Selection and Contrastive Learning |
Nazmul Karim; Mamshad Nayeem Rizve; Nazanin Rahnavard; Ajmal Mian; Mubarak Shah |
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| 170b | Contrastive Conditional Neural Processes | Zesheng Ye; Lina Yao | |
| 171b | One-Bit Active Query With Contrastive Pairs |
Yuhang Zhang; Xiaopeng Zhang; Lingxi Xie; Jie Li; Robert C. Qiu; Hengtong Hu; Qi Tian |
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| 172b | HCSC: Hierarchical Contrastive Selective Coding |
Yuanfan Guo; Minghao Xu; Jiawen Li; Bingbing Ni; Xuanyu Zhu; Zhenbang Sun; Yi Xu |
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| 173b | Motion-Aware Contrastive Video Representation Learning via Foreground-Background Merging |
Shuangrui Ding; Maomao Li; Tianyu Yang; Rui Qian; Haohang Xu; Qingyi Chen; Jue Wang; Hongkai Xiong |
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| 174b | Hierarchical Self-Supervised Representation Learning for Movie Understanding |
Fanyi Xiao; Kaustav Kundu; Joseph Tighe; Davide Modolo |
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| 175b | Anomaly Detection via Reverse Distillation From One-Class Embedding | Hanqiu Deng; Xingyu Li | |
| 176b | Unsupervised Representation Learning for Binary Networks by Joint Classifier Learning | Dahyun Kim; Jonghyun Choi | |
| 177b | DC-SSL: Addressing Mismatched Class Distribution in Semi-Supervised Learning |
Zhen Zhao; Luping Zhou; Yue Duan; Lei Wang; Lei Qi; Yinghuan Shi |
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| 178b | Learning To Collaborate in Decentralized Learning of Personalized Models |
Shuangtong Li; Tianyi Zhou; Xinmei Tian; Dacheng Tao |
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| 179b | Highly-Efficient Incomplete Large-Scale Multi-View Clustering With Consensus Bipartite Graph |
Siwei Wang; Xinwang Liu; Li Liu; Wenxuan Tu; Xinzhong Zhu; Jiyuan Liu; Sihang Zhou; En Zhu |
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| 180b | DASO: Distribution-Aware Semantics-Oriented Pseudo-Label for Imbalanced Semi-Supervised Learning | Youngtaek Oh; Dong-Jin Kim; In So Kweon | |
| 181b | Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning |
Haoxiang Wang; Yite Wang; Ruoyu Sun; Bo Li |
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| 182b | Semi-Supervised Object Detection via Multi-Instance Alignment With Global Class Prototypes | Aoxue Li; Peng Yuan; Zhenguo Li | |
| 183b | Unbiased Teacher v2: Semi-Supervised Object Detection for Anchor-Free and Anchor-Based Detectors | Yen-Cheng Liu; Chih-Yao Ma; Zsolt Kira | |
| 184b | Spectral Unsupervised Domain Adaptation for Visual Recognition |
Jingyi Zhang; Jiaxing Huang; Zichen Tian; Shijian Lu |
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| 185b | DATA: Domain-Aware and Task-Aware Self-Supervised Learning |
Qing Chang; Junran Peng; Lingxi Xie; Jiajun Sun; Haoran Yin; Qi Tian; Zhaoxiang Zhang |
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| 186b | Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-Learning |
Arnav Chavan; Rishabh Tiwari; Udbhav Bamba; Deepak K. Gupta |
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| 187b | DeepDPM: Deep Clustering With an Unknown Number of Clusters |
Meitar Ronen; Shahaf E. Finder; Oren Freifeld |
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| 188b | PLAD: Learning To Infer Shape Programs With Pseudo-Labels and Approximate Distributions |
R. Kenny Jones; Homer Walke; Daniel Ritchie |
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| 189b | Robust Outlier Detection by De-Biasing VAE Likelihoods |
Kushal Chauhan; Barath Mohan U; Pradeep Shenoy; Manish Gupta; Devarajan Sridharan |
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| 190b | Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data |
Corentin Sautier; Gilles Puy; Spyros Gidaris; Alexandre Boulch; Andrei Bursuc; Renaud Marlet |
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| 191b | CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding |
Mohamed Afham; Isuru Dissanayake; Dinithi Dissanayake; Amaya Dharmasiri; Kanchana Thilakarathna; Ranga Rodrigo |
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| 192b | Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentation |
Huan Gao; Jichang Guo; Guoli Wang; Qian Zhang |
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| 193b | DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation | Lukas Hoyer; Dengxin Dai; Luc Van Gool | |
| 194b | WildNet: Learning Domain Generalized Semantic Segmentation From the Wild |
Suhyeon Lee; Hongje Seong; Seongwon Lee; Euntai Kim |
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| 195b | UCC: Uncertainty Guided Cross-Head Co-Training for Semi-Supervised Semantic Segmentation | Jiashuo Fan; Bin Gao; Huan Jin; Lihui Jiang | |
| 196b | Semi-Supervised Semantic Segmentation With Error Localization Network | Donghyeon Kwon; Suha Kwak | |
| 197b | Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation |
Dayan Guan; Jiaxing Huang; Aoran Xiao; Shijian Lu |
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| 198b | Integrative Few-Shot Learning for Classification and Segmentation | Dahyun Kang; Minsu Cho | |
| 199b | GanOrCon: Are Generative Models Useful for Few-Shot Segmentation? |
Oindrila Saha; Zezhou Cheng; Subhransu Maji |
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| 200b | SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis |
Tianyi Chen; Yunfei Zhang; Xiaoyang Huo; Si Wu; Yong Xu; Hau San Wong |
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| 201b | CoordGAN: Self-Supervised Dense Correspondences Emerge From GANs |
Jiteng Mu; Shalini De Mello; Zhiding Yu; Nuno Vasconcelos; Xiaolong Wang; Jan Kautz; Sifei Liu |
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| Privacy and Federated Learning | 202b | GradViT: Gradient Inversion of Vision Transformers |
Ali Hatamizadeh; Hongxu Yin; Holger R. Roth; Wenqi Li; Jan Kautz; Daguang Xu; Pavlo Molchanov |
| 203b | Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them From 2D Renderings |
Innfarn Yoo; Huiwen Chang; Xiyang Luo; Ondrej Stava; Ce Liu; Peyman Milanfar; Feng Yang |
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| 204b | CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning | Yiqing Shen; Yuyin Zhou; Lequan Yu | |
| 205b | APRIL: Finding the Achilles’ Heel on Privacy for Vision Transformers |
Jiahao Lu; Xi Sheryl Zhang; Tianli Zhao; Xiangyu He; Jian Cheng |
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| 206b | Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning |
Liangqiong Qu; Yuyin Zhou; Paul Pu Liang; Yingda Xia; Feifei Wang; Ehsan Adeli; Li Fei-Fei; Daniel Rubin |
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| 207b | Robust Federated Learning With Noisy and Heterogeneous Clients | Xiuwen Fang; Mang Ye | |
| 208b | Federated Learning With Position-Aware Neurons |
Xin-Chun Li; Yi-Chu Xu; Shaoming Song; Bingshuai Li; Yinchuan Li; Yunfeng Shao; De-Chuan Zhan |
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| 209b | Layer-Wised Model Aggregation for Personalized Federated Learning |
Xiaosong Ma; Jie Zhang; Song Guo; Wenchao Xu |
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| 210b | FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning |
Minxue Tang; Xuefei Ning; Yitu Wang; Jingwei Sun; Yu Wang; Hai Li; Yiran Chen |
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| 211b | FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction |
Liang Gao; Huazhu Fu; Li Li; Yingwen Chen; Ming Xu; Cheng-Zhong Xu |
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| 212b | Differentially Private Federated Learning With Local Regularization and Sparsification |
Anda Cheng; Peisong Wang; Xi Sheryl Zhang; Jian Cheng |
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| 213b | Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage |
Zhuohang Li; Jiaxin Zhang; Luyang Liu; Jian Liu |
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| 214b | Learn From Others and Be Yourself in Heterogeneous Federated Learning | Wenke Huang; Mang Ye; Bo Du | |
| 215b | RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning |
Xiaoxiao Liang; Yiqun Lin; Huazhu Fu; Lei Zhu; Xiaomeng Li |
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| 216b | Federated Class-Incremental Learning |
Jiahua Dong; Lixu Wang; Zhen Fang; Gan Sun; Shichao Xu; Xiao Wang; Qi Zhu |
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| 217b | Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning |
Lin Zhang; Li Shen; Liang Ding; Dacheng Tao; Ling-Yu Duan |
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| 218b | FedCorr: Multi-Stage Federated Learning for Label Noise Correction |
Jingyi Xu; Zihan Chen; Tony Q.S. Quek; Kai Fong Ernest Chong |
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| 219b | ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning |
Jingtao Li; Adnan Siraj Rakin; Xing Chen; Zhezhi He; Deliang Fan; Chaitali Chakrabarti |
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| Explainable Computer Vision | 220b | Cycle-Consistent Counterfactuals by Latent Transformations | Saeed Khorram; Li Fuxin |
| 221b | Consistent Explanations by Contrastive Learning |
Vipin Pillai; Soroush Abbasi Koohpayegani; Ashley Ouligian; Dennis Fong; Hamed Pirsiavash |
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| 222b | Towards Better Understanding Attribution Methods | Sukrut Rao; Moritz Böhle; Bernt Schiele | |
| 223b | Proto2Proto: Can You Recognize the Car, the Way I Do? |
Monish Keswani; Sriranjani Ramakrishnan; Nishant Reddy; Vineeth N Balasubramanian |
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| 224b | Do Explanations Explain? Model Knows Best |
Ashkan Khakzar; Pedram Khorsandi; Rozhin Nobahari; Nassir Navab |
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| 225b | HINT: Hierarchical Neuron Concept Explainer | Andong Wang; Wei-Ning Lee; Xiaojuan Qi | |
| 226b | Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes |
Jon Donnelly; Alina Jade Barnett; Chaofan Chen |
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| 227b | What Do Navigation Agents Learn About Their Environment? |
Kshitij Dwivedi; Gemma Roig; Aniruddha Kembhavi; Roozbeh Mottaghi |
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| 228b | A Framework for Learning Ante-Hoc Explainable Models via Concepts |
Anirban Sarkar; Deepak Vijaykeerthy; Anindya Sarkar; Vineeth N Balasubramanian |
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| 229b | Exploiting Explainable Metrics for Augmented SGD |
Mahdi S. Hosseini; Mathieu Tuli; Konstantinos N. Plataniotis |
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| 230b | FAM: Visual Explanations for the Feature Representations From Deep Convolutional Networks |
Yuxi Wu; Changhuai Chen; Jun Che; Shiliang Pu |
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| 231b | Interactive Disentanglement: Learning Concepts by Interacting With Their Prototype Representations |
Wolfgang Stammer; Marius Memmel; Patrick Schramowski; Kristian Kersting |
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| 232b | B-Cos Networks: Alignment Is All We Need for Interpretability | Moritz Böhle; Mario Fritz; Bernt Schiele | |
| 233b | The Flag Median and FlagIRLS |
Nathan Mankovich; Emily J. King; Chris Peterson; Michael Kirby |
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| Transparency, Fairness, Accountability, Privacy & Ethics in Vision | 234b | Learning Fair Classifiers With Partially Annotated Group Labels |
Sangwon Jung; Sanghyuk Chun; Taesup Moon |
| 235b | Estimating Structural Disparities for Face Models |
Shervin Ardeshir; Cristina Segalin; Nathan Kallus |
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| 236b | Estimating Example Difficulty Using Variance of Gradients |
Chirag Agarwal; Daniel D'souza; Sara Hooker |
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| 237b | Fairness-Aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models |
Zhibo Wang; Xiaowei Dong; Henry Xue; Zhifei Zhang; Weifeng Chiu; Tao Wei; Kui Ren |
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| 238b | Fair Contrastive Learning for Facial Attribute Classification |
Sungho Park; Jewook Lee; Pilhyeon Lee; Sunhee Hwang; Dohyung Kim; Hyeran Byun |
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| 239b | Leveraging Adversarial Examples To Quantify Membership Information Leakage |
Ganesh Del Grosso; Hamid Jalalzai; Georg Pichler; Catuscia Palamidessi; Pablo Piantanida |
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| 240b | Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers |
Dominik Zietlow; Michael Lohaus; Guha Balakrishnan; Matthäus Kleindessner; Francesco Locatello; Bernhard Schölkopf; Chris Russell |
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| 241b | Deep Unlearning via Randomized Conditionally Independent Hessians |
Ronak Mehta; Sourav Pal; Vikas Singh; Sathya N. Ravi |
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| 242b | Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets |
Vishnu Suresh Lokhande; Rudrasis Chakraborty; Sathya N. Ravi; Vikas Singh |
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| 243b | A Study on the Distribution of Social Biases in Self-Supervised Learning Visual Models |
Kirill Sirotkin; Pablo Carballeira; Marcos Escudero-Viñolo |
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| Vision & X | 244b | Cross-Modal Perceptionist: Can Face Geometry Be Gleaned From Voices? |
Cho-Ying Wu; Chin-Cheng Hsu; Ulrich Neumann |
| 245b | Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation |
Xian Liu; Qianyi Wu; Hang Zhou; Yinghao Xu; Rui Qian; Xinyi Lin; Xiaowei Zhou; Wayne Wu; Bo Dai; Bolei Zhou |
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| 246b | SEEG: Semantic Energized Co-Speech Gesture Generation |
Yuanzhi Liang; Qianyu Feng; Linchao Zhu; Li Hu; Pan Pan; Yi Yang |
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| 247b | Mix and Localize: Localizing Sound Sources in Mixtures | Xixi Hu; Ziyang Chen; Andrew Owens | |
| 248b | Reading To Listen at the Cocktail Party: Multi-Modal Speech Separation |
Akam Rahimi; Triantafyllos Afouras; Andrew Zisserman |
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| 249b | IntentVizor: Towards Generic Query Guided Interactive Video Summarization | Guande Wu; Jianzhe Lin; Claudio T. Silva | |
| 250b | M3L: Language-Based Video Editing via Multi-Modal Multi-Level Transformers |
Tsu-Jui Fu; Xin Eric Wang; Scott T. Grafton; Miguel P. Eckstein; William Yang Wang |
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| 251b | Finding Fallen Objects via Asynchronous Audio-Visual Integration |
Chuang Gan; Yi Gu; Siyuan Zhou; Jeremy Schwartz; Seth Alter; James Traer; Dan Gutfreund; Joshua B. Tenenbaum; Josh H. McDermott; Antonio Torralba |
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| 252b | Weakly Paired Associative Learning for Sound and Image Representations via Bimodal Associative Memory | Sangmin Lee; Hyung-Il Kim; Yong Man Ro | |
| 253b | Egocentric Deep Multi-Channel Audio-Visual Active Speaker Localization |
Hao Jiang; Calvin Murdock; Vamsi Krishna Ithapu |
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| 254b | Audio-Visual Generalised Zero-Shot Learning With Cross-Modal Attention and Language |
Otniel-Bogdan Mercea; Lukas Riesch; A. Sophia Koepke; Zeynep Akata |
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| 255b | It’s Time for Artistic Correspondence in Music and Video |
Dídac Surís; Carl Vondrick; Bryan Russell; Justin Salamon |
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| 256b | Self-Supervised Object Detection From Audio-Visual Correspondence |
Triantafyllos Afouras; Yuki M. Asano; Francois Fagan; Andrea Vedaldi; Florian Metze |
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| 257b | More Than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech |
Michael Hassid; Michelle Tadmor Ramanovich; Brendan Shillingford; Miaosen Wang; Ye Jia; Tal Remez |
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| 258b | ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer |
Ruohan Gao; Zilin Si; Yen-Yu Chang; Samuel Clarke; Jeannette Bohg; Li Fei-Fei; Wenzhen Yuan; Jiajun Wu |
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| 259b | A Probabilistic Graphical Model Based on Neural-Symbolic Reasoning for Visual Relationship Detection |
Dongran Yu; Bo Yang; Qianhao Wei; Anchen Li; Shirui Pan |