PARC: Joint Modeling of Patch Appearance and Regional Composition for Training-Free Structural and Logical Anomaly Detection

In real-world industrial anomaly detection, production lines may contain products exhibiting both structural and logical anomalies. While conventional anomaly detection methods perform well in detecting structural anomalies, they often struggle with logical anomalies. Recent methods alleviate this limitation but often require category-specific training or auxiliary modules, which can limit generalizability and introduce substantial computational overhead. To overcome these limitations, we propose $\text{PARC}$, a training-free framework that jointly models $\text{P}$atch $\text{A}$ppearance and $\text{R}$egional $\text{C}$omposition using shared normal references. For appearance assessment, PARC evaluates local deviations by matching patch features against normal references. For composition assessment, it groups normal features into recurring patterns, summarizes each image by the proportions of these patterns within each spatial region, and scores how unusual these regional proportions are relative to normal images. Because structural and logical anomalies may occur independently, PARC calibrates the two scores using normal score distributions and fuses them for effective detection. Without requiring auxiliary modules or category-specific training, PARC achieves a combined image-level AUROC of 91.12% on MVTec-LOCO-AD using frozen features and raises the mean AUROC using five different backbones from 72.69% to 84.74%.