Multi-instance Active Learning

Multi-Instance Active Learning is an active learning paradigm in which the learner iteratively selects bags of instances, rather than individual instances, for labeling, with each bag possibly containing multiple instances and only the bag-level labels being available during the learning process.

21

Publications

1.5K

Citations

76

Authors

48

Institutions

Publications per year

2017–2026

9

Variants

Mial

Authors

76

  1. KG
    Kristen Grauman

    The University of Texas at Austin, United States

    2 publications · 170 citations

    2

    h-index

  2. SV
    Sudheendra Vijayanarasimhan

    The University of Texas at Austin, United States

    2 publications · 170 citations

    2

    h-index

  3. YS
    Yingcheng Sun

    Case Western Reserve University, United States

    1 publications · 17 citations

    1

    h-index

  4. CI
    Clara I. Sá‎nchez

    Radboud University Nijmegen, Netherlands

    1 publications · 60 citations

    1

    h-index

  5. HA
    Helen Ayles

    London School of Hygiene & Tropical Medicine, United Kingdom

    1 publications · 60 citations

    1

    h-index

1–5 of 76

Venues

  1. 2009 IEEE Conference on Computer Vision and Pattern Recognition

    Conference · 2 publications · 170 citations

    2

    h-index

  2. Pattern Recognition

    2 publications · 76 citations

    2

    h-index

  3. IEEE Transactions on Cybernetics

    1 publications · 94 citations

    1

    h-index

  4. Knowledge and Information Systems

    1 publications · 51 citations

    1

    h-index

  5. 1

    h-index

1–5