Long-tailed Action Recognition

Long-Tailed Action Recognition is an action recognition paradigm that addresses the challenge of learning from imbalanced datasets by distinguishing actions under a long-tailed class distribution where a few categories dominate while the majority are underrepresented.

17

Publications

598

Citations

78

Authors

30

Institutions

Publications per year

2017–2026

17

Authors

78

  1. YW
    Yizhou Wang

    University of Washington, United States

    2 publications · 143 citations

    2

    h-index

  2. JH
    Jenq–Neng Hwang

    University of Washington, United States

    2 publications · 143 citations

    2

    h-index

  3. JC
    Jiarui Cai

    University of Washington, United States

    2 publications · 143 citations

    2

    h-index

  4. EH
    E Haihong

    Beijing University of Posts and Telecommunications, China

    1 publications · 15 citations

    1

    h-index

  5. LL
    Lewei Lu

    Shanghai Artificial Intelligence Laboratory

    1 publications · 43 citations

    1

    h-index

1–5 of 78

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 39 citations

    3

    h-index

  2. Pattern Recognition

    3 publications · 48 citations

    3

    h-index

  3. 2

    h-index

  4. International Journal of Computer Vision

    1 publications · 145 citations

    1

    h-index

  5. Computer Methods and Programs in Biomedicine

    1 publications · 15 citations

    1

    h-index

1–5