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
46
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Jongheon Jeong, Taewan Kim, Dongqing Zhang et al. · 2023 · 363 citations
Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika et al. · arXiv (Cornell University) · 2019 · 179 citations · Full text
Unsupervised anomaly segmentation via deep feature reconstruction
Yong Shi, Jie Yang, Zhiquan Qi · Neurocomputing · 2020 · 173 citations · Full text
1–5 of 46
182
3
h-index
3
h-index
2
h-index
2
h-index
2
h-index
1–5 of 182
65

Chinese Academy of Sciences
Beijing, China
3 h-index
7 publications
712 citations
Fudan University
Shanghai, China
3 h-index
11 publications
916 citations

Hunan University
Changsha, China
2 h-index
5 publications
123 citations
University of Nebraska at Omaha
Omaha, United States
2 h-index
2 publications
226 citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
6 publications
82 citations
Tongji University
Shanghai, China
2 h-index
11 publications
362 citations

Southern University of Science and Technology
Shenzhen, China
2 h-index
9 publications
393 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
2 h-index
4 publications
435 citations
1–8 of 65
Repository · 11 publications · 890 citations
10
h-index
2 publications · 258 citations
2
h-index
2 publications · 77 citations
2
h-index
Conference · 1 publications · 16 citations
1
h-index
1 publications · 15 citations
1
h-index
1–5