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This paper presents a novel framework, named Global-Local CorrespondenceFramework (GLCF), for visual anomaly detection with logical constraints. Visualanomaly detection has become an active research area in various real-worldapplications, such as industrial anomaly detection and medical diseasediagnosis. However, most existing methods focus on identifying local structuraldegeneration anomalies and often fail to detect high-level functional anomaliesthat involve logical constraints. To address this issue, we propose atwo-branch approach that consists of a local branch for detecting structuralanomalies and a global branch for detecting logical anomalies. To facilitatelocal-global feature correspondence, we introduce a novel semantic bottleneckenabled by the visual Transformer. Moreover, we develop feature estimationnetworks for each branch separately to detect anomalies. Our proposed frameworkis validated using various benchmarks, including industrial datasets, Mvtec AD,Mvtec Loco AD, and the Retinal-OCT medical dataset. Experimental results showthat our method outperforms existing methods, particularly in detecting logicalanomalies.

Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection