ROAD: Unified Referring Operation Anomaly Detection via Cross-scene Dual Memory

Existing operation video anomaly detectors are bound to specific scenes and require retraining when the viewpoint, objects, or procedure changes. To tackle this limitation, we formulate the task of referring operation anomaly detection, where a detector scores a test video against a reference video of the valid operation, adapting to new scenarios solely by swapping the reference video. We release a dataset of 569 continuous one-take recordings from four scenarios, with annotated reference–test pairs and frame-level labels covering four process anomalies: wrong order, wrong placement, omitted step, and wrong component. We further propose a cross-scene dual-memory framework that separates target-specific progression in a reference-anchored temporal memory from cross-scene normality in a learnable prototype memory, fused by gated residual reconstruction. It reaches 91.63% mean AUROC across the three leave-one-scenario-out settings and 88.80% on a real-world packaging scenario, exceeding the previous strongest baselines by over 19.20 points. Code will be public after acceptance.