Semi-supervised Semantic Segmentation

Semi-Supervised Semantic Segmentation is a machine learning approach within pixel-level image understanding that leverages both a small set of labeled images and a larger set of unlabeled images to train models, distinguishing itself from fully-supervised methods by reducing reliance on extensive manual annotations while maintaining high segmentation accuracy.

264

Publications

20.3K

Citations

1.1K

Authors

343

Institutions

Publications per year

2017–2026

255

Authors

1.1K

  1. YW
    Yunchao Wei

    Beijing Jiaotong University, China

    6 publications · 803 citations

    6

    h-index

  2. LS
    Ling Shao

    Inception Institute of Artificial Intelligence, United Arab Emirates

    6 publications · 1.7K citations

    6

    h-index

  3. YZ
    Yi Zhou

    Southeast University, China

    5 publications · 1.7K citations

    5

    h-index

  4. LZ
    Luping Zhou

    The University of Sydney, Australia

    5 publications · 526 citations

    5

    h-index

  5. JW
    Jingdong Wang

    Microsoft Research Asia (China), China

    5 publications · 1.4K citations

    5

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 49 publications · 2.5K citations

    24

    h-index

  2. 12

    h-index

  3. Remote Sensing

    11 publications · 362 citations

    10

    h-index

  4. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 9 publications · 1K citations

    9

    h-index

  5. 7

    h-index

1–5