Papers › Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization
Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization
Yunkang Cao, Xiaohao Xu, Zhaoge Liu, Weiming Shen
Most unsupervised image anomaly localization methods suffer from overgeneralization because of the high generalization abilities of convolutional neural networks, leading to unreliable predictions. To mitigate the overgeneralization, this study proposes to collaboratively optimize normal and abnormal feature distributions with the assistance of synthetic anomalies, namely collaborative discrepancy optimization (CDO). CDO introduces a margin optimization module and an overlap optimization module to optimize the two key factors determining the localization performance, i.e., the margin and the overlap between the discrepancy distributions (DDs) of normal and abnormal samples. With CDO, a large margin and a small overlap between normal and abnormal DDs are obtained, and the prediction reliability is boosted. Experiments on MVTec2D and MVTec3D show that CDO effectively mitigates the overgeneralization and achieves great anomaly localization performance with real-time computation efficiency. A real-world automotive plastic parts inspection application further demonstrates the capability of the proposed CDO. Code is available on https://github.com/caoyunkang/CDO.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MVTEC 3D-AD | CDO | Segmentation AUPRO | 93.75 | #1 of 2 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CDO | FPS | 79.6 (exclude data inputting time), 18.7 (contain data inputting time) | #124 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CDO | Segmentation AUPRO | 96.50 | #124 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CDO | Segmentation AUROC | 98.70 | #124 of 148 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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