Weakly Supervised Labeling

Weakly Supervised Labeling is an image labeling paradigm that belongs to the broader category of supervised learning methods, distinguished by its reliance on incomplete, inexact, or imprecise supervision signals—such as image-level labels instead of pixel-level annotations—thereby reducing the dependency on densely annotated training data.

150

Publications

12.3K

Citations

561

Authors

220

Institutions

Publications per year

2017–2026

116

Authors

561

  1. RB
    Rodrigo Benenson

    Max Planck Institute for Informatics, Germany

    4 publications · 298 citations

    4

    h-index

  2. AK
    Anna Khoreva

    Max Planck Institute for Informatics, Germany

    4 publications · 298 citations

    4

    h-index

  3. BS
    Bernt Schiele

    Max Planck Institute for Informatics, Germany

    4 publications · 298 citations

    4

    h-index

  4. KY
    Kim–Hui Yap

    Nanyang Technological University, Singapore

    3 publications · 190 citations

    3

    h-index

  5. SF
    Songhe Feng

    Beijing Jiaotong University, China

    3 publications · 100 citations

    3

    h-index

1–5 of 561

Venues

  1. arXiv (Cornell University)

    Repository · 21 publications · 824 citations

    13

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 220 citations

    4

    h-index

  3. IEEE Transactions on Image Processing

    4 publications · 177 citations

    4

    h-index

  4. International Journal of Computer Vision

    4 publications · 158 citations

    4

    h-index

  5. Pattern Recognition Letters

    3 publications · 170 citations

    3

    h-index

1–5