BiaS: Incorporating Biased Knowledge to Boost Unsupervised Image Anomaly Localization-Reference-Cited by-同舟云学术

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BiaS: Incorporating Biased Knowledge to Boost Unsupervised Image Anomaly Localization

Published:2024-04 Issue:4 Volume:54 Page:2342-2353
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Cao Yunkang¹^ORCID,Xu Xiaohao²^ORCID,Sun Chen³^ORCID,Gao Liang¹^ORCID,Shen Weiming¹^ORCID

Affiliation:

1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China

2. Michigan Robotics, University of Michigan, Ann Arbor, MI, USA

3. Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Canada

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221021/10474538/10402554.pdf?arnumber=10402554

Reference41 articles.

1. The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

2. Computer Vision Techniques in Manufacturing

3. IM-IAD: Industrial image anomaly detection benchmark in manufacturing;Xie;arXiv:2301.13359,2023

4. Real3D-AD: A dataset of point cloud anomaly detection;Liu;arXiv:2309.13226,2023

5. Integrated Inspection on PCB Manufacturing in Cyber–Physical–Social Systems

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2. Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection;Engineering Applications of Artificial Intelligence;2024-11

3. Dual-path Frequency Discriminators for few-shot anomaly detection;Knowledge-Based Systems;2024-10

4. Prior Normality Prompt Transformer for Multiclass Industrial Image Anomaly Detection;IEEE Transactions on Industrial Informatics;2024

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