Region-Prompt-Guided Anomaly Detection With Entropy-Based Consistency Modeling

Visual industrial anomaly detection has evolved from one-class modeling to more challenging multi-class settings, where diverse categories and complex visual patterns must be jointly handled. Existing approaches often assume that anomalies lie far from normal samples in feature or spatial space. However, this assumption frequently fails due to two key issues: cross-class semantic confusion, where normal structures of one category are misclassified as anomalies in another, and pixel similarity failure, where anomalous regions visually blend into normal backgrounds. To address these challenges, we propose RPGAD (Region-Prompt Guided Anomaly Detection), an information-theoretic framework that models anomalies as semantic predictive instability, reflected in the joint responses of dual paths. RPGAD integrates two components: 1) DPENet (Dual-Path regional Energy evaluation Network), which compares region-level responses across normal-only and mixed paths through an entropy-guided energy formulation to generate robust region prompts; and 2) RDNet (Reverse Distillation Network), which selectively reconstructs prompted regions and employs a Prototype-Contrastive Optimal Transport (PCOT) loss to enhance inter-class separability and local feature aggregation. Experiments on five anomaly detection benchmarks - MVTecAD, VisA, BTAD, MPDD, and Real-IAD - demonstrate the effectiveness of RPGAD. At $256 \times 256$ resolution, RPGAD achieves strong overall performance, with mAD of 87.8%, 80.3%, 85.2%, 86.2%, and 77.7% on five benchmarks, and pixel-level AP and F1-max gains of up to 12.2 and 10.4 points over strong baselines. These results confirm that RPGAD provides accurate and robust multi-class anomaly detection and localization in complex visual scenarios.