Interactive Instance Segmentation

Interactive Instance Segmentation is an instance segmentation paradigm that iteratively refines object masks through human-in-the-loop feedback, distinguishing itself from autonomous methods by incorporating user-provided inputs such as clicks or corrections to guide the segmentation process.

36

Publications

1.8K

Citations

138

Authors

89

Institutions

Publications per year

2017–2026

22

Authors

138

  1. SD
    Suyog Dutt Jain

    The University of Texas at Austin, United States

    3 publications · 71 citations

    3

    h-index

  2. KG
    Kristen Grauman

    The University of Texas at Austin, United States

    3 publications · 71 citations

    3

    h-index

  3. BP
    Brian Price

    Adobe Systems (United States), United States

    3 publications · 197 citations

    3

    h-index

  4. SW
    Shikui Wei

    Beijing Jiaotong University, China

    2 publications · 148 citations

    2

    h-index

  5. YZ
    Yao Zhao

    Beijing Jiaotong University, China

    2 publications · 148 citations

    2

    h-index

1–5 of 138

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 41 citations

    3

    h-index

  2. Medical Image Analysis

    2 publications · 274 citations

    2

    h-index

  3. International Journal of Computer Vision

    2 publications · 45 citations

    2

    h-index

  4. Pattern Recognition

    2 publications · 81 citations

    2

    h-index

  5. Signal Processing Image Communication

    1 publications · 11 citations

    1

    h-index

1–5