MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities
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MemSeg only introduces anomalies in external datasets in the foreground of items, which is another reason for its excellent performance.
“In order to prevent the influence of background factors, MemSeg only introduces anomalies in external datasets in the foreground of items, which is another reason for its excellent performance.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [18]
With the exception of MemSeg [18], the training of other one-class classification methods relies on SVDD and cross-entropy loss.
“As demonstrated in Table 3, with the exception of MemSeg [18] , the training of other methods relies on SVDD and cross-entropy loss.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [18]
CutPaste, DRAEM, and MemSeg are representative methods of unsupervised anomaly detection using data augmentation to synthesize anomaly images.
“For examples, CutPaste [31] , DRAEM [91] , MemSeg [18] are representative methods.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [18]
Without using ensemble learning, MemSeg achieved the SOTA result on image-level anomaly classification.
“Without using ensemble learning, MemSeg [18] achieved the SOTA result on image-level anomaly classification”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [18]
Many unsupervised anomaly detection works use data augmentation to synthesize anomaly images and significantly improve model performance.
“There are many unsupervised anomaly detection works that use data augmentation to synthetic anomaly images and significantly improve model performance.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [18]
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WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
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Deep Industrial Image Anomaly Detection: A Survey
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- Authors
- Yang, M., Wu, P., & Feng, H.
- Affiliations
- Xidian University (CN)
- Northwestern Polytechnical University (CN)
- Corresponding author
- Yang, Minghui
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 119
- Pages
- 105835
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W4320476215
- Cited by
- 257
- References
- 30
- Citations by year
- 2026: 46 · 2025: 94 · 2024: 89 · 2023: 28
- FWCI
- 30.77
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Engineering Applications of Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- surface defect detection, semi-supervised learning, memory pool, intra-class variation, industrial visual inspection, real-time defect detection, MVTec AD
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