Video Self-supervised Learning

Video Self-Supervised Learning is a machine learning paradigm that enables models to learn spatiotemporal representations from unlabeled video data by leveraging the inherent temporal coherence and structure of video sequences as supervisory signals.

142

Publications

10.8K

Citations

572

Authors

206

Institutions

Publications per year

2017–2026

135

Authors

572

  1. AZ
    Andrew Zisserman

    University of Oxford, United Kingdom

    6 publications · 747 citations

    6

    h-index

  2. CS
    Cordelia Schmid

    Institut national de recherche en sciences et technologies du numérique, France

    5 publications · 1.3K citations

    5

    h-index

  3. DL
    Dezhao Luo

    University of Chinese Academy of Sciences, China

    5 publications · 394 citations

    5

    h-index

  4. YZ
    Yu Zhou

    Harbin Institute of Technology, China

    5 publications · 394 citations

    5

    h-index

  5. LW
    Limin Wang

    Nanjing University, China

    5 publications · 920 citations

    5

    h-index

1–5 of 572

Venues

  1. arXiv (Cornell University)

    Repository · 25 publications · 1.5K citations

    16

    h-index

  2. 13

    h-index

  3. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 10 publications · 354 citations

    10

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 7 publications · 623 citations

    7

    h-index

  5. IEEE Transactions on Image Processing

    5 publications · 478 citations

    5

    h-index

1–5