Semi-supervised Annotation

Semi-Supervised Annotation is a machine learning approach to image annotation that combines a small set of labeled data with a large set of unlabeled data to improve annotation accuracy and efficiency by leveraging both supervised and unsupervised learning mechanisms.

200

Publications

9.2K

Citations

751

Authors

312

Institutions

Publications per year

2017–2026

123

Authors

751

  1. GQ
    Guo-Jun Qi

    University of Central Florida, United States

    10 publications · 635 citations

    10

    h-index

  2. XH
    Xian‐Sheng Hua

    Microsoft Research Asia (China), China

    8 publications · 474 citations

    9

    h-index

  3. JT
    Jinhui Tang

    Nanjing University of Science and Technology, China

    8 publications · 812 citations

    8

    h-index

  4. YS
    Yan Song

    University of Science and Technology of China, China

    5 publications · 168 citations

    6

    h-index

  5. TM
    Tao Mei

    Microsoft Research Asia (China), China

    5 publications · 223 citations

    5

    h-index

1–5 of 751

Venues

  1. IEEE Transactions on Multimedia

    9 publications · 827 citations

    9

    h-index

  2. arXiv (Cornell University)

    Repository · 9 publications · 215 citations

    9

    h-index

  3. Bioinformatics

    7 publications · 244 citations

    8

    h-index

  4. IEEE Access

    6 publications · 233 citations

    8

    h-index

  5. Sensors

    4 publications · 154 citations

    4

    h-index

1–5