Weakly Supervised Image Classification

Weakly Supervised Image Classification is an image classification paradigm that, unlike fully supervised approaches requiring precise pixel-level or instance-level annotations, relies on coarse-grained or incomplete supervision—such as image-level labels—to train models capable of recognizing or localizing objects within images.

375

Publications

49.7K

Citations

1.4K

Authors

466

Institutions

Publications per year

2017–2026

313

Authors

1.4K

  1. YW
    Yunchao Wei

    Beijing Jiaotong University, China

    8 publications · 2.1K citations

    9

    h-index

  2. QY
    Qixiang Ye

    University of Chinese Academy of Sciences, China

    7 publications · 1.1K citations

    7

    h-index

  3. XW
    Xinggang Wang

    Huazhong University of Science and Technology, China

    5 publications · 565 citations

    6

    h-index

  4. AV
    Andrea Vedaldi

    University of Oxford, United Kingdom

    6 publications · 6K citations

    6

    h-index

  5. HL
    Haojie Li

    Dalian University of Technology, China

    6 publications · 320 citations

    6

    h-index

1–5 of 1.4K

Venues

  1. arXiv (Cornell University)

    Repository · 43 publications · 8.1K citations

    23

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 13 publications · 2.3K citations

    13

    h-index

  3. IEEE Transactions on Medical Imaging

    11 publications · 552 citations

    11

    h-index

  4. Neurocomputing

    9 publications · 292 citations

    9

    h-index

  5. IEEE Transactions on Image Processing

    9 publications · 1.7K citations

    9

    h-index

1–5