The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Reconstruction by inpainting for visual anomaly detection
Abstract
Abstract Visual anomaly detection addresses the problem of classification or localization of regions in an image that deviate from their normal appearance. A popular approach trains an auto-encoder on anomaly-free images and performs anomaly detection by calculating the difference between the input and the reconstructed image. This approach assumes that the auto-encoder will be unable to accurately reconstruct anomalous regions. But in practice neural networks generalize well even to anomalies and reconstruct them sufficiently well, thus reducing the detection capabilities. Accurate reconstruction is far less likely if the anomaly pixels were not visible to the auto-encoder. We thus cast anomaly detection as a self-supervised reconstruction-by-inpainting problem. Our approach (RIAD) randomly removes partial image regions and reconstructs the image from partial inpaintings, thus addressing the drawbacks of auto-enocoding methods. RIAD is extensively evaluated on several benchmarks and sets a new state-of-the art on a recent highly challenging anomaly detection benchmark.
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Cites
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Cited by
DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection
Zavrtanik, V., Kristan, M., & Skocaj, D. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00822 · Cited by 1,086
Self-citationAnomaly Detection via Reverse Distillation from One-Class Embedding
Deng, H., & Li, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00951 · Cited by 892
Deep 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
“RIAD [87] randomly masks a portion of the training set image at the patch level and reconstructs it using a U-Net encoder-decoder network [92].”
MethodInfluentialMixed supervision for surface-defect detection: From weakly to fully supervised learning
Božič, J., Tabernik, D., & Skočaj, D. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103459 · Cited by 377
Self-citationAnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
Gu, Z., Zhu, B., Zhu, G., Chen, Y., Tang, M., & Wang, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27963 · Cited by 298
Metadata
- Authors
- Zavrtanik, V., Kristan, M., & Skočaj, D.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 112
- Pages
- 107706
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W3092704883
- Cited by
- 732
- References
- 42
- FWCI
- 20.36
- Type
- journal article
- Language
- en
- Open access
- closed
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