Weakly Supervised Object Recognition

Weakly Supervised Object Recognition is an object recognition paradigm that, unlike fully supervised approaches, relies on incomplete, imprecise, or noisy label information—such as image-level labels without bounding box annotations—to learn accurate object detectors or localizers.

149

Publications

23.3K

Citations

452

Authors

176

Institutions

Publications per year

2017–2026

101

Authors

452

  1. FW
    Fang Wan

    University of Chinese Academy of Sciences, China

    9 publications · 966 citations

    9

    h-index

  2. JC
    Junsuk Choe

    Naver (South Korea), South Korea

    8 publications · 6.1K citations

    8

    h-index

  3. QY
    Qixiang Ye

    University of Chinese Academy of Sciences, China

    7 publications · 793 citations

    7

    h-index

  4. YW
    Yunchao Wei

    Beijing Jiaotong University, China

    6 publications · 1.6K citations

    6

    h-index

  5. HS
    Hyunjung Shim

    Yonsei University, South Korea

    6 publications · 759 citations

    6

    h-index

1–5 of 452

Venues

  1. arXiv (Cornell University)

    Repository · 26 publications · 1.4K citations

    17

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 10 publications · 1.3K citations

    10

    h-index

  3. IEEE Transactions on Image Processing

    8 publications · 1.6K citations

    8

    h-index

  4. 4

    h-index

  5. Pattern Recognition

    4 publications · 199 citations

    4

    h-index

1–5