Vision Self-supervised Learning

Vision Self-Supervised Learning is a machine learning paradigm that belongs to the broader category of self-supervised learning, distinguished by its focus on learning rich visual representations from unlabeled image or video data through automatically generated supervision signals derived from the intrinsic structure of visual inputs.

296

Publications

30.8K

Citations

1.1K

Authors

286

Institutions

Publications per year

2017–2026

280

Authors

1.1K

  1. PB
    Piotr Bojanowski

    Meta (Israel), Israel

    10 publications · 7.1K citations

    10

    h-index

  2. SX
    Saining Xie

    Meta (Israel), Israel

    8 publications · 3.7K citations

    8

    h-index

  3. IM
    Ishan Misra

    Meta (Israel), Israel

    8 publications · 6.7K citations

    8

    h-index

  4. YL
    Yann LeCun

    New York University, United States

    6 publications · 667 citations

    6

    h-index

  5. IS
    In So Kweon

    Korea Advanced Institute of Science and Technology, South Korea

    5 publications · 1.5K citations

    6

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 95 publications · 7.1K citations

    37

    h-index

  2. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 10 publications · 7.1K citations

    10

    h-index

  3. IEEE Robotics and Automation Letters

    7 publications · 453 citations

    9

    h-index

  4. 7

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 162 citations

    6

    h-index

1–5