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Registration Based Few-Shot Anomaly Detection
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“There are two settings in FSAD. The first setting is meta-learning [141] .”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [141]
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WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
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Self-citation
Metadata
- Authors
- Huang, C., Guan, H., Jiang, A., Zhang, Y., Spratling, M., & Wang, Y.-F.
- Affiliations
- National University of Singapore (SG)
- King's College London (GB)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Zhang, Ya
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 303-319
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4312298392
- Cited by
- 216
- References
- 39
- Citations by year
- 2026: 60 · 2025: 71 · 2024: 64 · 2023: 21
- FWCI
- 21.57
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Science and Technology Commission of Shanghai Municipality (BP0719010, 18DZ2270700)
- National Key Research and Development Program of China (BP0719010)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- few-shot anomaly detection, image registration, anomaly detection, MVTec
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