Call for Papers

We invite submissions of both long and short papers on the topic of out-of-distribution generalization in computer vision. The long papers should use the ECCV 2022 template and should be max. 14 pages long. The short papers should use the CVPR template and be max. 4 pages long. The topics include but are not limited to:
  • Improving generalization of computer vision systems in OOD scenarios
  • Research at the intersection of biological and machine vision
  • Generative causal models for image analysis
  • Domain generalization
  • Novel architectures with robustness to occlusion, viewpoint and other real-world domain shifts
  • Domain adaptation techniques for robust vision system in the real world
  • Datasets for evaluating model robustness

Please submit you paper to the https://cmt3.research.microsoft.com/OODCV2022/Submission/Index.

Important Dates

Description Date
Paper submission deadline August 8th, 2022 (AoE)
Notification to authors August 16th, 2022 (AoE)
Camera-ready deadline August 22nd, 2022 (AoE)

Accepted Papers

Best Papers

Best Long Paper

  • Domain-Conditioned Normalization for Test-Time Domain Generalization [pdf]

    Authors: Yuxuan Jiang (SJTU), Yan-Feng Wang (SJTU), Ruipeng Zhang (SJTU), Qinwei Xu (SJTU), Ya Zhang (SJTU), Xin Chen (Huawei), Qi Tian (Huawei)

Best Short Paper

  • The Value of Out-of-Distribution Data [pdf]

    Authors: Ashwin De Silva (JHU), Rahul Ramesh (UPENN), Carey E Priebe (JHU), Pratik Chaudhari (UPENN), Joshua T Vogelstein (JHU)

Long Papers

  • Domain-Conditioned Normalization for Test-Time Domain Generalization [pdf]

    Authors: Yuxuan Jiang (SJTU), Yan-Feng Wang (SJTU), Ruipeng Zhang (SJTU), Qinwei Xu (SJTU), Ya Zhang (SJTU), Xin Chen (Huawei), Qi Tian (Huawei)

  • Unleashing the Potential of Adaptation Models via Go-getting Domain Labels [pdf] [supp]

    Authors: Xin Jin(YITech), Tianyu He(Alibaba), Xu Shen(Alibaba), Songhua Wu(USYD), Tongliang Liu(USYD), Jingwen Ye(NUS), Xinchao Wang(NUS), Jianqiang Huang(Alibaba), Zhibo Chen(NUS), Xian-Sheng Hua(Alibaba)

  • ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization [pdf]

    Authors: Zdravko Marinov (KIT), Alina Roitberg (KIT), David Schneider (KIT), Rainer Stiefelhagen (KIT)

  • Consistency Regularization for Domain Adaptation [pdf]

    Authors: Kian Boon Koh (A*STAR), Basura Fernando (A*STAR)

Short Papers

  • CycDA: Unsupervised Cycle Domain Adaptation to Learn from Image to Video [pdf]

    Authors: Wei Lin(TU Graz), Anna Kukleva(MPI-INF), Kunyang Sun(TU Graz), Horst Possegger(TU Graz), Hilde Kuehne(Goethe-Universität), Horst Bischof(TU Graz)

  • Take One Gram of Neural Features, Get Enhanced Group Robustness [pdf]

    Authors: Simon Roburin*(École des Ponts), Charles Corbière*(Cnam), Gilles Puy(valeo.ai), Nicolas Thome(Cnam), Matthieu Aubry(École des Ponts), Renaud Marlet(valeo.ai), Patrick Pérez(valeo.ai)

  • Domain-Specific Risk Minimization [pdf]

    Authors: Yi-Fan Zhang(CASIA), Jindong Wang(MSRA), Zhang Zhang(CASIA), Baosheng Yu(USYD), Liang Wang(CASIA), Dacheng Tao(USYD), Xing Xie(MSRA)

  • Generalizable Person Re-identification Without Demographics [pdf] [supp]

    Authors: Yi-Fan Zhang(CASIA), Zhang Zhang(CASIA), Baosheng Yu(USYD), Liang Wang(CASIA), Dacheng Tao(USYD), Tieniu Tan(CASIA)

  • Learning Robust Representations via Nuisance-extended Information Bottleneck [pdf]

    Authors: Jongheon Jeong (KAIST), Sihyun Yu (KAIST), Hankook Lee (KAIST), Jinwoo Shin (KAIST)

  • Semantic Self-adaptation: Enhancing Generalization with a Single Sample [pdf]

    Authors: Sherwin Bahmani*(TU Darmstadt), Oliver Hahn*(TU Darmstadt), Eduard Zamfir*, Nikita Araslanov(TU Munich), Daniel Cremers(TU Munich), Stefan Roth(hessian.AI)

  • Feed-Forward Source-Free Domain Adaptation via Class Prototypes [pdf]

    Authors: Ondrej Bohdal(Edin), Da Li(Samsung AI Center), Timothy Hospedales(Edin)

  • INDIGO: Intrinsic Multimodality for Domain Generalization [pdf]

    Authors: Puneet Mangla*(Adobe Research), Shivam Chandhok*(Universite Grenoble Alpes), Milan Aggarwal(Adobe Research), Vineeth N Balasubramanian(IIT Delhi), Balaji Krishnamurthy(Adobe Research)

  • The Value of Out-of-Distribution Data [pdf]

    Authors: Ashwin De Silva (JHU)*, Rahul Ramesh (UPENN), Carey E Priebe (JHU), Pratik Chaudhari (UPENN), Joshua T Vogelstein (JHU)

  • A Closer Look at Smoothness in Domain Adversarial Training [pdf]

    Authors: Harsh Rangwani*(IISc), Sumukh K Aithal*(IISc), Mayank Mishra(IISc), Arihant Jain(IISc)