Multi-instance Image Retrieval

Multi-Instance Image Retrieval is an image retrieval paradigm in which a single query corresponds to multiple instance-level visual samples, and the system must identify target images by modeling intra-query variability and distinguishing relevant instances from irrelevant ones within a shared semantic context.

32

Publications

1.4K

Citations

94

Authors

45

Institutions

Publications per year

2017–2026

5

Authors

94

  1. MS
    Mei‐Ling Shyu

    University of Miami, United States

    3 publications · 77 citations

    3

    h-index

  2. SA
    Sally A. Goldman

    Washington University in St. Louis, United States

    3 publications · 379 citations

    3

    h-index

  3. CS
    Chun-Rong Su

    National Taiwan University of Science and Technology, Taiwan

    2 publications · 85 citations

    2

    h-index

  4. JC
    Jiann‐Jone Chen

    National Taiwan University of Science and Technology, Taiwan

    2 publications · 85 citations

    2

    h-index

  5. JP
    Jinye Peng

    Northwest University, China

    2 publications · 28 citations

    2

    h-index

1–5 of 94

Venues

  1. Neurocomputing

    3 publications · 76 citations

    3

    h-index

  2. IEEE Transactions on Image Processing

    3 publications · 156 citations

    3

    h-index

  3. Pattern Recognition

    2 publications · 70 citations

    2

    h-index

  4. 1

    h-index

  5. Multimedia Systems

    1 publications · 12 citations

    1

    h-index

1–5