Anomaly Segmentation

Anomaly Segmentation is a time series analysis task that involves partitioning a temporal sequence into contiguous regions to identify and delineate intervals exhibiting anomalous behavior, distinguished from point-based anomaly detection by its focus on localized segments rather than isolated timestamps.

46

Publications

3.6K

Citations

182

Authors

65

Institutions

Publications per year

2017–2026

40

Authors

182

  1. YS
    Yong Shi

    Chinese Academy of Sciences, China

    3 publications · 273 citations

    3

    h-index

  2. ZQ
    Zhiquan Qi

    Chinese Academy of Sciences, China

    3 publications · 273 citations

    3

    h-index

  3. JW
    Jinbao Wang

    Southern University of Science and Technology, China

    2 publications · 79 citations

    2

    h-index

  4. DH
    Dan Hendrycks

    University of California, Berkeley, United States

    2 publications · 229 citations

    2

    h-index

  5. DZ
    Dongqing Zhang

    Guangdong University of Petrochemical Technology, China

    2 publications · 376 citations

    2

    h-index

1–5 of 182

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 890 citations

    10

    h-index

  2. Neurocomputing

    2 publications · 258 citations

    2

    h-index

  3. 2

    h-index

  4. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 1 publications · 16 citations

    1

    h-index

  5. Journal of Intelligent Information Systems

    1 publications · 15 citations

    1

    h-index

1–5