Prototype–GVA–IDAC: Prototype-Based Localization and Image-Level Discrimination for Cross-Dataset Industrial Anomaly Detection

Industrial visual anomaly detection models are required not only to localize subtle defects in unseen products and acquisition environments, but also to provide reliable image-level predictions. However, appearance shifts in cross-dataset settings can weaken the correspondence between textual semantics and local visual evidence. Moreover, MultiADS directly averages the global text-based anomaly probability and the maximum response of the anomaly map, making it difficult to simultaneously achieve fine-grained localization and stable discrimination. To address these limitations, we propose Prototype–GVA–IDAC, built upon MultiADS with frozen CLIP encoders. Prototype enhances multi-level patch representations through soft prototype assignment, contextual reconstruction, and residual refinement, enabling local anomaly cues to transfer more robustly to unseen datasets. GVA independently aggregates global image features and multi-level local statistics, thereby providing complementary visual discriminative evidence for image-level prediction. IDAC separately calibrates normal and anomalous text anchors under identity-preservation and normal–anomaly separation constraints, reducing the discrepancy between fixed textual semantics and target-domain images. The model is trained on MVTec AD and evaluated on five datasets—BTAD, MPDD, KSDD2, VisA, and WFDD—without using any target-domain data for training intervention, hyperparameter selection, or threshold tuning throughout the entire process. Experimental results demonstrate that, under the same evaluation protocol, the proposed model exhibits clear advantages in cross-dataset defect localization and anomaly discrimination.