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ORCIDCVPR 2022
- IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022. IEEE 2022, ISBN 978-1-6654-6946-3
- Meina Zhang, Yingying Fang, Guoxi Ni, Tieyong Zeng:
Pixel screening based intermediate correction for blind deblurring. 1-9 - Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, Serge J. Belongie:
When Does Contrastive Visual Representation Learning Work? 1-10 - Dengpan Fu, Dongdong Chen, Hao Yang, Jianmin Bao, Lu Yuan, Lei Zhang, Houqiang Li, Fang Wen, Dong Chen:
Large-Scale Pre-training for Person Re-identification with Noisy Labels. 1-11 - Yunhui Guo, Xudong Wang, Yubei Chen, Stella X. Yu:
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers. 1-10 - Yunhui Guo, Haoran Guo, Stella X. Yu:
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data. 11-20 - Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, Zhao Zhang:
Efficient Deep Embedded Subspace Clustering. 21-30 - Jiexi Yan, Lei Luo, Chenghao Xu, Cheng Deng, Heng Huang:
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning. 31-40 - Kun-Peng Ning, Xun Zhao, Yu Li
, Sheng-Jun Huang:
Active Learning for Open-set Annotation. 41-49 - Theodoros Tsiligkaridis, Jay Roberts:
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training. 50-59 - Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein:
Robust Optimization as Data Augmentation for Large-scale Graphs. 60-69 - Sihao Yu, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Zizhen Wang, Xueqi Cheng:
A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty. 70-79 - Bingyuan Liu, Ismail Ben Ayed, Adrian Galdran, Jose Dolz:
The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration. 80-88 - Guoliang Lin, Hanlu Chu, Hanjiang Lai:
Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector. 89-98 - Rishabh Tiwari, KrishnaTeja Killamsetty, Rishabh K. Iyer, Pradeep Shenoy:
GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning. 99-108 - Qingsen Yan, Dong Gong, Yuhang Liu, Anton van den Hengel, Javen Qinfeng Shi:
Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning. 109-118 - Daniel Grzech, Mohammad Farid Azampour, Ben Glocker, Julia A. Schnabel, Nassir Navab, Bernhard Kainz, Loïc Le Folgoc:
A variational Bayesian method for similarity learning in non-rigid image registration. 119-128 - Yadong Ding, Yu Wu, Chengyue Huang, Siliang Tang, Yi Yang, Longhui Wei, Yueting Zhuang, Qi Tian:
Learning to Learn by Jointly Optimizing Neural Architecture and Weights. 129-138 - Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer G. Dy, Tomas Pfister:
Learning to Prompt for Continual Learning. 139-149 - Mengqi Xue, Haofei Zhang, Jie Song, Mingli Song:
Meta-attention for ViT-backed Continual Learning. 150-159 - Vitor Guizilini, Rares Ambrus, Dian Chen, Sergey Zakharov, Adrien Gaidon:
Multi-Frame Self-Supervised Depth with Transformers. 160-170 - Zhen Wang, Liu Liu, Yiqun Duan, Yajing Kong, Dacheng Tao:
Continual Learning with Lifelong Vision Transformer. 171-181 - Jianfeng Wang, Thomas Lukasiewicz:
Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation. 182-190 - Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, Luc Van Gool:
Revisiting Random Channel Pruning for Neural Network Compression. 191-201 - Huayi Tang, Yong Liu:
Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase. 202-211 - Jongin Lim, Sangdoo Yun, Seulki Park, Jin Young Choi:
Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Learning. 212-222 - Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, Shadab Khan:
Towards Robust and Reproducible Active Learning using Neural Networks. 223-232 - Jiulong Liu, Zhaoqiang Liu:
Non-Iterative Recovery from Nonlinear Observations using Generative Models. 233-243 - Minyoung Kim:
Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders. 244-253 - Kwang In Kim:
Robust Combination of Distributed Gradients Under Adversarial Perturbations. 254-263 - Lan Wang, Vishnu Naresh Boddeti:
Do learned representations respect causal relationships? 264-274 - Rafid Mahmood, James Lucas, David Acuna, Daiqing Li, Jonah Philion, José M. Álvarez, Zhiding Yu, Sanja Fidler, Marc T. Law:
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks. 275-284 - Magzhan Gabidolla, Miguel Á. Carreira-Perpiñán:
Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees. 285-294 - Dian Chen, Dequan Wang, Trevor Darrell, Sayna Ebrahimi:
Contrastive Test-Time Adaptation. 295-305 - Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, Shubham Tulsiani:
AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation. 306-315 - Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu:
Selective-Supervised Contrastive Learning with Noisy Labels. 316-325 - Yufei Guo, Xinyi Tong, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Zhe Ma, Xuhui Huang:
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks. 326-335 - M. Saquib Sarfraz, Marios Koulakis, Constantin Seibold, Rainer Stiefelhagen:
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction. 336-345 - Yikai Wang, Xinwei Sun, Yanwei Fu:
Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels. 346-355 - Xiran Fan, Chun-Hao Yang, Baba C. Vemuri:
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design. 356-365 - Ivor J. A. Simpson, Sara Vicente, Neill D. F. Campbell:
Learning Structured Gaussians to Approximate Deep Ensembles. 366-374 - Ruoyu Wang, Mingyang Yi, Zhitang Chen, Shengyu Zhu:
Out-of-distribution Generalization with Causal Invariant Transformations. 375-385 - Tom Ryder, Chen Zhang, Ning Kang, Shifeng Zhang:
Split Hierarchical Variational Compression. 386-395 - Iordanis Fostiropoulos, Barry W. Boehm:
Implicit Feature Decoupling with Depthwise Quantization. 396-405 - Jurijs Nazarovs, Zhichun Huang, Songwong Tasneeyapant, Rudrasis Chakraborty, Vikas Singh:
Understanding Uncertainty Maps in Vision with Statistical Testing. 406-416 - Anh-Dzung Doan, Michele Sasdelli, David Suter, Tat-Jun Chin:
A Hybrid Quantum-Classical Algorithm for Robust Fitting. 417-427 - Paul Roetzer, Paul Swoboda, Daniel Cremers, Florian Bernard:
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching. 428-438 - Ahmed Abbas, Paul Swoboda:
FastDOG: Fast Discrete Optimization on GPU. 439-449 - Vladimir Chikin, Mikhail Antiukh:
Data-Free Network Compression via Parametric Non-uniform Mixed Precision Quantization. 450-459 - Huu Le, Rasmus Kjær Høier, Che-Tsung Lin, Christopher Zach:
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks. 460-469 - Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman, Vladimir G. Kim, Matthew Fisher, Niloy J. Mitra:
GLASS: Geometric Latent Augmentation for Shape Spaces. 470-479 - Matteo Spallanzani, Gian Paolo Leonardi, Luca Benini:
Training Quantised Neural Networks with STE Variants: the Additive Noise Annealing Algorithm. 470-479 - Nuo Xu, Jianlong Chang, Xing Nie, Chunlei Huo, Shiming Xiang, Chunhong Pan:
AME: Attention and Memory Enhancement in Hyper-Parameter Optimization. 480-489 - Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, Benjamin D. Haeffele:
Efficient Maximal Coding Rate Reduction by Variational Forms. 490-498 - Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Florian Bernard, Daniel Cremers:
A Unified Framework for Implicit Sinkhorn Differentiation. 499-508 - Yidong Chen, Chen Li, Zhonghua Lu:
Computing Wasserstein-$p$ Distance Between Images with Linear Cost. 509-518 - Natacha Kuete Meli, Florian Mannel, Jan Lellmann:
An Iterative Quantum Approach for Transformation Estimation from Point Sets. 519-527 - Nourhan Bayasi, Ghassan Hamarneh, Rafeef Garbi:
BoosterNet: Improving Domain Generalization of Deep Neural Nets using Culpability-Ranked Features. 528-538 - Dong-Hwan Jang, Sanghyeok Chu, Joonhyuk Kim, Bohyung Han:
Pooling Revisited: Your Receptive Field is Suboptimal. 539-548 - Jiajing Chen, Burak Kakillioglu, Huantao Ren, Senem Velipasalar:
Why Discard if You can Recycle?: A Recycling Max Pooling Module for 3D Point Cloud Analysis. 549-557 - Mu Hu, Junyi Feng, Jiashen Hua, Baisheng Lai, Jianqiang Huang, Xiaojin Gong, Xiansheng Hua:
Online Convolutional Reparameterization. 558-567 - Xiaohan Ding, Honghao Chen, Xiangyu Zhang, Jungong Han, Guiguang Ding:
RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality. 568-577 - Tao Huang, Shan You, Bohan Zhang, Yuxuan Du, Fei Wang, Chen Qian, Chang Xu:
DyRep: Bootstrapping Training with Dynamic Re-parameterization. 578-587 - Tianlong Chen, Zhenyu Zhang, Yihua Zhang, Shiyu Chang, Sijia Liu, Zhangyang Wang:
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free. 588-599 - Anil Kag, Venkatesh Saligrama:
Condensing CNNs with Partial Differential Equations. 600-609 - Shaojie Bai, Zhengyang Geng, Yash Savani, J. Zico Kolter:
Deep Equilibrium Optical Flow Estimation. 610-620 - Matan Atzmon, Koki Nagano, Sanja Fidler, Sameh Khamis, Yaron Lipman:
Frame Averaging for Equivariant Shape Space Learning. 621-631 - Gee-Sern Hsu, Chun-Hung Tsai, Hung-Yi Wu:
Dual-Generator Face Reenactment. 632-640 - Rongzhen Zhao, Jian Li, Zhenzhi Wu:
Convolution of Convolution: Let Kernels Spatially Collaborate. 641-650 - Matthias Wödlinger, Jan Kotera, Jan Xu, Robert Sablatnig:
SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention. 651-660 - Michael Schelling, Pedro Hermosilla, Timo Ropinski:
RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising. 661-670 - Utkarsh Singhal, Yifei Xing, Stella X. Yu:
Co-domain Symmetry for Complex-Valued Deep Learning. 671-680 - Tong Yu, Ruslan Khalitov, Lei Cheng, Zhirong Yang:
Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention. 681-690 - Huanyu Wang, Junjie Liu, Xin Ma, Yang Yong, Zhenhua Chai, Jianxin Wu:
Compressing Models with Few Samples: Mimicking then Replacing. 691-700 - Raymond A. Yeh, Yuan-Ting Hu, Zhongzheng Ren, Alexander G. Schwing:
Total Variation Optimization Layers for Computer Vision. 701-711 - Vinit Veerendraveer Singh, Chandra Kambhamettu:
AIM: an Auto-Augmenter for Images and Meshes. 712-721 - George Yiasemis, Jan-Jakob Sonke, Clarisa Sánchez, Jonas Teuwen:
Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction. 722-731 - Nicolas Donati, Etienne Corman, Maks Ovsjanikov:
Deep orientation-aware functional maps: Tackling symmetry issues in Shape Matching. 732-741 - Jingqi Huang, Yue Ning, Dong Nie, Linan Guan, Xiping Jia:
Weakly-supervised Metric Learning with Cross-Module Communications for the Classification of Anterior Chamber Angle Images. 742-752 - Lei Huang, Yi Zhou, Tian Wang, Jie Luo, Xianglong Liu:
Delving into the Estimation Shift of Batch Normalization in a Network. 753-762 - Fanqing Lin, Brian L. Price, Tony R. Martinez:
Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks. 763-772 - Wenshuo Li, Hanting Chen, Jianyuan Guo, Ziyang Zhang, Yunhe Wang:
Brain-inspired Multilayer Perceptron with Spiking Neurons. 773-783 - Koushik Biswas, Sandeep Kumar, Shilpak Banerjee, Ashish Kumar Pandey:
Smooth Maximum Unit: Smooth Activation Function for Deep Networks using Smoothing Maximum Technique. 784-793 - Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross B. Girshick, Piotr Dollár, Laurens van der Maaten:
Revisiting Weakly Supervised Pre-Training of Visual Perception Models. 794-804 - Xuran Pan, Chunjiang Ge, Rui Lu, Shiji Song, Guanfu Chen, Zeyi Huang, Gao Huang:
On the Integration of Self-Attention and Convolution. 805-815 - Jianyuan Guo, Yehui Tang, Kai Han, Xinghao Chen, Han Wu, Chao Xu, Chang Xu, Yunhe Wang:
Hire-MLP: Vision MLP via Hierarchical Rearrangement. 816-826 - Benjamin Naoto Chiche, Arnaud Woiselle, Joana Frontera-Pons, Jean-Luc Starck:
Stable Long-Term Recurrent Video Super-Resolution. 827-836 - Aming Wu, Cheng Deng:
Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation. 837-846 - Anlin Zheng, Yuang Zhang, Xiangyu Zhang, Xiaojuan Qi, Jian Sun:
Progressive End-to-End Object Detection in Crowded Scenes. 847-856 - Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole:
Zero-Shot Text-Guided Object Generation with Dream Fields. 857-866 - Mingjin Zhang, Rui Zhang, Yuxiang Yang, Haichen Bai, Jing Zhang, Jie Guo:
ISNet: Shape Matters for Infrared Small Target Detection. 867-876 - Yi-Nan Chen, Hang Dai, Yong Ding:
Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. 877-887 - Tu Zheng, Yifei Huang, Yang Liu, Wenjian Tang, Zheng Yang, Deng Cai, Xiaofei He:
CLRNet: Cross Layer Refinement Network for Lane Detection. 888-897 - Yanan Zhang, Jiaxin Chen, Di Huang:
CAT-Det: Contrastively Augmented Transformer for Multimodal 3D Object Detection. 898-907 - Yu-Jhe Li, Jinhyung Park, Matthew O'Toole, Kris Kitani:
Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection. 908-917 - Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Xiangnan He:
Group Contextualization for Video Recognition. 918-928 - Suchen Wang, Yueqi Duan, Henghui Ding, Yap-Peng Tan, Kim-Hui Yap, Junsong Yuan:
Learning Transferable Human-Object Interaction Detector with Natural Language Supervision. 929-938 - Gongjie Zhang, Zhipeng Luo, Yingchen Yu, Kaiwen Cui, Shijian Lu:
Accelerating DETR Convergence via Semantic-Aligned Matching. 939-948 - Jialian Wu, Sudhir Yarram, Hui Liang, Tian Lan, Junsong Yuan, Jayan Eledath, Gérard G. Medioni:
Efficient Video Instance Segmentation via Tracklet Query and Proposal. 949-958 - Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, Qianru Sun:
Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation. 959-968 - Siyue Yu, Jimin Xiao, Bingfeng Zhang, Eng Gee Lim:
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection. 969-978 - Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, Linlin Shen:
C2 AM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation. 979-988 - Ayan Kumar Bhunia, Subhadeep Koley, Abdullah Faiz Ur Rahman Khilji, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song:
Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval. 989-998 - Hao Li, Tianwen Fu, Jifeng Dai, Hongsheng Li, Gao Huang, Xizhou Zhu:
AutoLoss-Zero: Searching Loss Functions from Scratch for Generic Tasks. 999-1008 - Jihwan Park, Seungjun Lee, Hwan Heo, Hyeong Kyu Choi, Hyunwoo J. Kim:
Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection. 1009-1018 - Hanyu Xuan, Zhiliang Wu, Jian Yang, Yan Yan, Xavier Alameda-Pineda:
A Proposal-based Paradigm for Self-supervised Sound Source Localization in Videos. 1019-1028 - Canjie Luo, Lianwen Jin, Jingdong Chen:
SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization. 1029-1038 - Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis:
Towards End-to-End Unified Scene Text Detection and Layout Analysis. 1039-1049 - Xinqian Gu, Hong Chang, Bingpeng Ma, Shutao Bai, Shiguang Shan, Xilin Chen:
Clothes-Changing Person Re-identification with RGB Modality Only. 1050-1059 - Qing Lian, Peiliang Li, Xiaozhi Chen:
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection. 1060-1069 - Jiaqi Gu, Bojian Wu, Lubin Fan, Jianqiang Huang, Shen Cao, Zhiyu Xiang, Xian-Sheng Hua:
Homography Loss for Monocular 3D Object Detection. 1070-1079 - Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu, Chiew-Lan Tai:
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers. 1080-1089 - Ruihang Chu, Xiaoqing Ye, Zhengzhe Liu, Xiao Tan, Xiaojuan Qi, Chi-Wing Fu, Jiaya Jia:
TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation. 1090-1099 - Haiyang Wang, Shaoshuai Shi, Ze Yang, Rongyao Fang, Qi Qian, Hongsheng Li, Bernt Schiele, Liwei Wang:
RBGNet: Ray-based Grouping for 3D Object Detection. 1100-1109 - Yanwei Li, Xiaojuan Qi, Yukang Chen, Liwei Wang, Zeming Li, Jian Sun, Jiaya Jia:
Voxel Field Fusion for 3D Object Detection. 1110-1119 - Yurong You, Katie Luo, Cheng Perng Phoo, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark E. Campbell, Kilian Q. Weinberger:
Learning to Detect Mobile Objects from LiDAR Scans Without Labels. 1120-1130 - David Schinagl, Georg Krispel, Horst Possegger, Peter M. Roth, Horst Bischof:
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data. 1131-1140 - Yichun Shen, Wanli Jiang, Zhen Xu, Rundong Li, Junghyun Kwon:
Confidence Propagation Cluster: Unleash Full Potential of Object Detectors. 1141-1151 - Sijie Zhu, Mubarak Shah, Chen Chen:
TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization. 1152-1161 - Yongjian Deng, Hao Chen, Hai Liu, Youfu Li:
A Voxel Graph CNN for Object Classification with Event Cameras. 1162-1171 - Dongchen Lu, Dongmei Li, Yali Li, Shengjin Wang:
OSKDet: Orientation-sensitive Keypoint Localization for Rotated Object Detection. 1172-1182