StructCore: Structure-Aware Image-Level Scoring for Training-Free Unsupervised Anomaly Detection
This work addresses the limitation of conventional max-pooling–based image-level anomaly scoring methods, which often yield overlapping scores for normal and anomalous samples due to their neglect of spatial distribution and structural information in anomaly evidence. To overcome this, the authors propose StructCore, a training-free, structure-aware image-level scoring method that enhances discriminability without altering pixel-level anomaly localization. StructCore models the spatial layout of anomaly score maps using low-dimensional structural descriptors and incorporates diagonal Mahalanobis distance calibration derived from defect-free samples. As the first image-level scoring framework to integrate structural awareness, StructCore achieves state-of-the-art unsupervised performance, attaining image-level AUROC scores of 99.6% on MVTec AD and 98.4% on VisA.