| [1] ReMP: Rectified Metric Propagation for Few-Shot Learning |
Yang Zhao (University at Buffalo)*; Chunyuan Li (Microsoft Research, Redmond); Ping Yu (University at Buffalo); Changyou Chen (University at Buffalo) |
| [2] DAMSL: Domain Agnostic Meta Score-based Learning |
John Cai (Princeton University)*; Bill Cai (Massachusetts Institute of Technology); Shengmei Shen (Pensees AI institute of Singapore) |
| [3] Unsupervised Discriminative Embedding for Sub-Action Learning in Complex Activities |
Sirnam Swetha (University of Central Florida)*; Hilde Kuehne (University of Frankfurt); Yogesh Rawat (University of Central Florida); Mubarak Shah (University of Central Florida) |
| [4] One-Shot GAN: Learning to Generate Samples from Single Images and Videos |
Vadim Sushko (Bosch Center for Artificial Intelligence)*; Jürgen Gall (University of Bonn); Anna Khoreva (Bosch Center for Artificial Intelligence) |
| [5] Efficacy of Bayesian Neural Networks in Active Learning |
Vineeth Rakesh (Interdigital AI Lab)*; Swayambhoo Jain (Interdigital AI Lab) |
| [6] ProFeat: Unsupervised Image Clustering via Progressive Feature Refinement |
Jeonghoon Kim (DGIST)*; Sunghoon Im (DGIST); Sunghyun Cho (POSTECH) |
| [7] Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaption |
Chen-Hao Chao (National Tsing Hua University)*; Bo-Wun Cheng (National Tsing Hua University); Chun-Yi Lee (National Tsing Hua University) |
| [8] Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding |
Cesar Caiafa (CONICET/RIKEN AIP)*; Ziyao Wang (Southeast University); Jordi Solé-Casals (University of Vic–Central University of Catalonia); Qibin Zhao (RIKEN AIP) |
| [9] Training Deep Generative Models in Highly Incomplete Data Scenarios with Prior Regularization |
Edgar A Bernal (University of Rochester)* |
| [10] Unlocking the Full Potential of Small Data with Diverse Supervision |
Ziqi Pang (Peking University); Zhiyuan Hu (Tsinghua University)*; Pavel Tokmakov (Toyota Research Institute); Yu-Xiong Wang (University of Illinois at Urbana-Champaign); Martial Hebert (Carnegie Mellon School of Computer Science) |
| [11] BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains |
Or Patashnik (Tel Aviv University)*; Dov Danon (Tel Aviv University); Hao Zhang (Simon Fraser University); Danny Cohen-Or (Tel Aviv University) |
| [12] Shot in the Dark: Few-Shot Learning with No Base-Class Labels |
Zitian Chen (University of Massachusetts, Amherst)*; Subhransu Maji (University of Massachusetts, Amherst); Erik Learned-Miller (University of Massachusetts, Amherst) |
| [13] Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis |
Xiaoyu Xiang (Purdue University)*; Ding Liu (Bytedance); Xiaohui Shen (ByteDance AI Lab); Yiheng Zhu (ByteDance AI Lab); Xiao Yang (Bytedance AI Lab); Jan Allebach (Purdue University) |
| [14] Distill on the Go: Online knowledge distillation in self supervised learning |
Prashant Bhat (Advanced Research Lab, NavInfo Europe)*; Elahe Arani (Navinfo Europe ); Bahram Zonooz (Navinfo Europe) |
| [15] Boosting Co-teaching with Compression Regularization for Label Noise |
Yingyi Chen (KU Leuven)*; Xi Shen (École des Ponts ParisTech); Shell X Hu (Upload AI LLC); Johan Suykens (KU Leuven) |
| [16] Instance-Level Task Parameters: A Robust Multi-task Weighting Framework |
Pavan Kumar Anasosalu Vasu (Apple Inc.)*; Shreyas Saxena (Apple Inc.); Oncel Tuzel (Apple Inc.) |
| [17] A Closer Look at Self-training for Zero-Label Semantic Segmentation |
Giuseppe Pastore (Politecnico di Torino)*; Fabio Cermelli (Politecnico di Torino); Yongqin Xian (Max Planck Institute Informatics); Massimiliano Mancini (University of Tübingen); Zeynep Akata (University of Tübingen); Barbara Caputo (Politecnico di Torino) |
| [18] Contrastive Learning Improves Model Robustness Under Label Noise |
Aritra Ghosh (University of Massachusetts Amherst)*; Andrew Lan (University of Massachusetts Amherst) |
| [19] A Simple Framework for Cross-Domain Few-Shot Recognition with Unlabeled Data |
Ashraful Islam (Rensselaer Polytechnic Institute)*; Chun-Fu Richard Chen (MIT-IBM Watson AI Lab, IBM Research AI); Rameswar Panda (MIT-IBM Watson AI Lab, IBM Research); Leonid Karlinsky (IBM-Research); Rogerio Feris (MIT-IBM Watson AI Lab, IBM Research); Richard Radke (Rensselaer Polytechnic Institute) |
| [20] Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics |
Anurag Singh (NSIT); Naren Doraiswamy (University of Michigan, Ann Arbor); Sawa Takamuku (Aisin Seiki Co., ltd.); Megh M Bhalerao (National Institute of Technology, Karnataka); Titir Dutta (Indian Institute of Science, Bangalore); Soma Biswas (Indian Institute of Science, Bangalore)*; Aditya Chepuri (AISIN AUTOMOTIVE HARYANA PVT. LTD); Balasubramanian Vengatesan (Aisin Automotive Haryana Pvt Ltd); Naotake Natori (Aisin Seiki Co., Ltd.) |
| [21] Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation |
Youshan Zhang (Lehigh University)*; Brian D. Davison (Lehigh University) |
| [22] Learning Unbiased Representations via Mutual Information Backpropagation |
Ruggero Ragonesi (Istituto Italiano di Tecnologia)*; Riccardo Volpi (Naver Labs Europe); Jacopo Cavazza (Istituto Italiano di Tecnologia); Vittorio Murino (Istituto Italiano di Tecnologia) |
| [23] PLM: Partial Label Masking for Imbalanced Multi-label Classification |
Kevin Duarte (University of Central Florida)*; Yogesh Rawat (University of Central Florida); Mubarak Shah (University of Central Florida) |
| [24] Batch Normalization Embeddings for Deep Domain Generalization |
Mattia Segù (ETH Zurich)*; Alessio Tonioni (Google); Federico Tombari (Google, TU Munich) |
| [25] Cluster-driven Graph Federated Learning over Multiple Domains |
Debora Caldarola (Politecnico di Torino)*; Massimiliano Mancini (University of Tübingen); Fabio Galasso (Sapienza University); Marco Ciccone (Politecnico di Milano); Emanuele Rodola (Sapienza University of Rome); Barbara Caputo (Politecnico di Torino) |
| [26] Fine-grained Angular Contrastive Learning with Coarse Labels |
Guy Bukchin (Penta AI, Tel Aviv University)*; Eli Schwartz (IBM-Research); Kate Saenko (Boston University); Ori Shahar (Penta AI); Rogerio Feris (MIT-IBM Watson AI Lab, IBM Research); Raja Giryes (Tel Aviv University); Leonid Karlinsky (IBM-Research) |
| [27] Weak Multi-View Supervision for Surface Mapping Estimation |
Nishant Rai (Stanford University)*; Aidas Liaudanskas (Fyusion Inc.); Srinivas Rao (Fyusion, Inc.); Rodrigo J Ortiz Cayon (Fyusion); Matteo Munaro (Fyusion Inc.); Stefan Holzer (Fyusion Inc) |
| [28] Training Rare Object Detection in Satellite Imagery with Synthetic GAN Images |
Eric Martinson (Soar Technology)*; Andy Gillies (Soar Technology); Bridget Furlong (Soar Technology) |
| [29] An Exploration into why Output Regularization Mitigates Label Noise |
Neta Shoham (Edgify)*; Tomer Avidor (Edgify); Nadav Tal-Israel (Edgify) |
| [30] One-shot action recognition in challenging therapy scenarios |
Alberto Sabater (Universidad de Zaragoza)*; Laura Santos (Instituto Superior Técnico Universidade de Lisboa); Alexandre Bernardino (-); José Santos-Victor (Instituto Superior Técnico - ISR); Luis Montesano (University of Zaragoza; Bitbrain); Ana C Murillo (Universidad de Zaragoza) |
| [31] A causal view of compositional zero-shot recognition |
Yuval Atzmon (NVIDIA Research)*; Felix Kreuk (Bar-Ilan University); Uri Shalit (Technion); Gal Chechik (Bar Ilan University) |
| [32] TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition |
Rami Ben-Ari (OriginAI)*; Mor Shpigel Nacson (Technion); ophir azulai (IBM-Research); Udi Barzelay (IBM ); Daniel Rotman (IBM Research) |
| [33] Introducing Meta-Verbs into Graph Convolutional Networks for Zero-shot Action Recognition |
Chinmaya Devaraj (Univ of Maryland)*; Cornelia Fermuller (University of Maryland, College Park); Yiannis Aloimonos (University of Maryland, College Park) |
| [34] Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data |
Yichun Shi (Michigan State University)*; Anil Jain (Michigan State University) |
| [35] Challenge - Adapting Multi-source Representations for Cross-Domain Few-shot Learning
| Ge Liu (Shanghai Jiao Tong University)*; Xiangzhong Fang (Shanghai Jiao Tong University) |
| [36] Challenge - Team jszx101
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| [37] Challenge - Team TJU-VisionGroup
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| [38] Challenge - Team NJUST-JDExplore
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| [39] Challenge - Team Yonsei-CVPR
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