DeMuS: Learning Decoupled Matching and Scoring for Batch Zero-Shot Industrial Anomaly Detection

Zero-Shot Industrial Anomaly Detection (IAD) is criticalfor rapid deployment in agile manufacturing. We investigate a highlypractical Batch Zero-Shot IAD setting, strictly constrained by smallintra-batch references and common pose variations. Directly applyingexisting mutual-scoring methods fails because training-free features lackgeneralized anomaly semantics and spatial robustness, resulting in am-biguous scoring distances and an “anomaly-to-anomaly shortcut” duringmatching, which can lead to missed detections. To address this, we pro-pose DeMuS (Decoupled Mutual Scoring), a novel two-stage learningframework. By training two lightweight adapters via a two-stage aux-iliary process, our method extracts highly discriminative anomaly se-mantics for accurate scoring (how-to-score), while explicitly decouplingrobust part-level correspondence learning (where-to-match) to guaranteereliable physical alignment. This synergistic mechanism effectively pre-vents semantic confusion under unaligned inputs. Extensive experimentson MVTec AD, VisA, and Real-IAD demonstrate DeMuS achieves state-of-the-art performance and superior robustness against pose shifts, main-taining efficient inference for online inspection. Our code is available athttps://github.com/evoLonation/DeMuS.