Papers › Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and...

Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization

22 Feb 2022IJCV 2022 2archive 2025-07-28

Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, Carsten Steger

The unsupervised detection and localization of anomalies in natural images is an intriguing and challenging problem. Anomalies manifest themselves in very different ways and an ideal benchmark dataset for this task should contain representative examples for all of them. We find that existing datasets are biased towards local structural anomalies such as scratches, dents, or contaminations. In particular, they lack anomalies in the form of violations of logical constraints, e.g., permissible objects occurring in invalid locations. We contribute a new dataset based on industrial inspection scenarios that evenly covers both types of anomalies. We provide pixel-precise ground truth data for each anomalous region and define a generalized evaluation metric that addresses localization ambiguities that can arise for logical anomalies. Furthermore, we propose a novel algorithm that improves over the state of the art in the joint detection of structural and logical anomalies. It consists of a local and a global network branch. The first one inspects confined regions independent of their spatial locations in the input image and is primarily responsible for the detection of entirely new local structures. The second one learns a globally consistent representation of the training data through a bottleneck that enables the detection of violations of long-range dependencies, a key characteristic of many logical anomalies. We perform extensive evaluations on our new dataset to corroborate our claims.

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Tasks

Anomaly DetectionUnsupervised Anomaly Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Anomaly Detection MVTec LOCO AD GCAD Avg. Detection AUROC 83.3 #22 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD GCAD Detection AUROC (only logical) 86.0 #22 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD GCAD Detection AUROC (only structural) 80.6 #22 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD GCAD Segmentation AU-sPRO (until FPR 5%) 70.1 #22 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD Variation Model Avg. Detection AUROC 57.7 #37 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD Variation Model Detection AUROC (only logical) 56.5 #37 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD Variation Model Detection AUROC (only structural) 58.9 #37 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD Variation Model Segmentation AU-sPRO (until FPR 5%) 22.5 #37 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD L2AE Avg. Detection AUROC 57.3 #38 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD L2AE Detection AUROC (only logical) 58.1 #38 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD L2AE Detection AUROC (only structural) 56.5 #38 of 40 Archive leaderboard report
Anomaly Detection MVTec LOCO AD L2AE Segmentation AU-sPRO (until FPR 5%) 37.8 #38 of 40 Archive leaderboard report
Anomaly Detection VisA GCAD Detection AUROC 89.1 #30 of 50 Archive leaderboard report
Anomaly Detection VisA GCAD Segmentation AUPRO (until 30% FPR) 83.7 #30 of 50 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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