  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.
