Papers › Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection
Haiming Yao, Wenyong Yu, Wei Luo, Zhenfeng Qiang, Donghao Luo, Xiaotian Zhang
This paper presents a novel framework, named Global-Local Correspondence Framework (GLCF), for visual anomaly detection with logical constraints. Visual anomaly detection has become an active research area in various real-world applications, such as industrial anomaly detection and medical disease diagnosis. However, most existing methods focus on identifying local structural degeneration anomalies and often fail to detect high-level functional anomalies that involve logical constraints. To address this issue, we propose a two-branch approach that consists of a local branch for detecting structural anomalies and a global branch for detecting logical anomalies. To facilitate local-global feature correspondence, we introduce a novel semantic bottleneck enabled by the visual Transformer. Moreover, we develop feature estimation networks for each branch separately to detect anomalies. Our proposed framework is validated using various benchmarks, including industrial datasets, Mvtec AD, Mvtec Loco AD, and the Retinal-OCT medical dataset. Experimental results show that our method outperforms existing methods, particularly in detecting logical anomalies.
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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 LOCO AD | GLCF | Avg. Detection AUROC | 83.1 | #23 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | GLCF | Detection AUROC (only logical) | 82.4 | #23 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | GLCF | Detection AUROC (only structural) | 83.8 | #23 of 40 | Archive leaderboard | report |
| Anomaly Detection | MVTec LOCO AD | GLCF | Segmentation AU-sPRO (until FPR 5%) | 70.3 | #23 of 40 | 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.
Methods
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