SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation
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AU-PR is more valuable than AU-ROC for segmentation tasks.
“AU-PR is more valuable than AU-ROC for segmentation tasks [67] .”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [67]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581 · see citing works
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244 · see citing works
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309 · see citing works
Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00257-z · Cited by 2,224 · see citing works
Towards Total Recall in Industrial Anomaly Detection
Roth, K., Pemula, L., Zepeda, J., Scholkopf, B., Brox, T., & Gehler, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01392 · Cited by 1,810 · see citing works
Self-citationPaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization
Defard, T., Setkov, A., Loesch, A., & Audigier, R. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-68799-1_35 · Cited by 1,415 · see citing works
Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., & Steger, C. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01400-4 · Cited by 602 · see citing works
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WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., & Dabeer, O. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01878 · Cited by 585 · see citing works
Self-citationDeep Industrial Image Anomaly Detection: A Survey
Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., & Jin, Y. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1459-z · Cited by 463 · see citing works
ZUMA: Training-Free Zero-Shot Unified Multimodal Anomaly Detection
Ma, Y., Liu, M., Jiang, S., Zhou, J., Bian, Y., Wang, X., & Wang, Y. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3658856 · Cited by 4 · see citing works
- Authors
- Zou, Y., Jeong, J., Pemula, L., Zhang, D., & Dabeer, O.
- Affiliations
- Amazon (United States) (US)
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Zou, Yang
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 392-408
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4312605624
- Cited by
- 651
- References
- 9
- Citations by year
- 2026: 244 · 2025: 248 · 2024: 137 · 2023: 22
- FWCI
- 52.94
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image anomaly detection, anomaly segmentation, self-supervised pre-training, contrastive learning, industrial inspection, ImageNet pre-training, MVTec AD, one-class anomaly detection
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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