Image Anomaly Detection

Image Anomaly Detection is a computational framework within the field of computer vision that identifies deviations from normal patterns in visual data by leveraging models trained predominantly on normal instances to detect rare or unseen anomalies.

1.1K

Publications

69.2K

Citations

3.3K

Authors

1K

Institutions

Publications per year

2017–2026

804

Authors

3.3K

  1. AP
    Antonio Plaza

    Universidad de Extremadura, Spain

    23 publications · 2.7K citations

    21

    h-index

  2. QD
    Qian Du

    Mississippi State University, United States

    23 publications · 2.8K citations

    20

    h-index

  3. WX
    Weiying Xie

    Xidian University, China

    15 publications · 1.1K citations

    15

    h-index

  4. YL
    Yunsong Li

    Xidian University, China

    15 publications · 1.1K citations

    15

    h-index

  5. WL
    Wei Li

    Beijing Institute of Technology, China

    13 publications · 1.9K citations

    13

    h-index

1–5 of 3.3K

Venues

  1. 48

    h-index

  2. arXiv (Cornell University)

    Repository · 83 publications · 4.8K citations

    32

    h-index

  3. IEEE Geoscience and Remote Sensing Letters

    GRSL · 38 publications · 1.8K citations

    25

    h-index

  4. 20

    h-index

  5. Remote Sensing

    26 publications · 836 citations

    19

    h-index

1–5