Weakly Supervised Object Detection

Weakly Supervised Object Detection is an object detection paradigm that belongs to the broader class of supervised learning methods but is distinguished by its reliance on incomplete or coarse-grained supervision, such as image-level labels instead of precise bounding box annotations, to localize and recognize objects within images.

358

Publications

30.6K

Citations

1.1K

Authors

345

Institutions

Publications per year

2017–2026

310

Authors

1.1K

  1. JH
    Junwei Han

    Northwestern Polytechnical University, China

    19 publications · 2.1K citations

    19

    h-index

  2. GC
    Gong Cheng

    Northwestern Polytechnical University, China

    15 publications · 1.7K citations

    15

    h-index

  3. FW
    Fang Wan

    University of Chinese Academy of Sciences, China

    13 publications · 1.3K citations

    13

    h-index

  4. QY
    Qixiang Ye

    University of Chinese Academy of Sciences, China

    12 publications · 1.3K citations

    12

    h-index

  5. XY
    Xiwen Yao

    Northwestern Polytechnical University, China

    11 publications · 703 citations

    11

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 47 publications · 2K citations

    22

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 20 publications · 2.3K citations

    18

    h-index

  3. IEEE Transactions on Image Processing

    19 publications · 1.7K citations

    16

    h-index

  4. 14

    h-index

  5. 12

    h-index

1–5