CLUE-VAD: Structured Semantic Clues for Understanding Explainable Events in Video Anomaly Detection

Weakly Supervised Video Anomaly Detection aims to detect abnor-mal events in long, untrimmed videos using only video-level supervision, provid-ing a practical solution to the high cost of temporally localized annotations. How-ever, abnormal events in real-world surveillance are often determined by complexcontextual interactions among actions, objects, and environments, making it dif-ficult for existing approaches to identify which factors actually cause an anomaly.Most prior methods rely on holistic representations or explanations that lack ex-plicit attribution to the semantic factors driving abnormal events. In this paper,we present CLUE-VAD, a structured semantic decomposition framework for ex-plainable weakly supervised video anomaly detection. Rather than relying solelyon holistic video representations or free-form explanations, CLUE-VAD decom-poses each video segment into structured semantic clues—Action, Environment,and Object—and estimates their category-aware contributions to anomaly predic-tion. By linking anomaly scores to clue-specific weights and keyword-level evi-dence, we provides factor-level attribution and supports transparent explanationgeneration. Extensive experiments on VAD benchmarks demonstrate that CLUE-VAD achieves strong detection performance among X-VAD methods while pro-viding fine-grained, clue-grounded explanations for anomaly predictions. Theproject page is available at https://visualsciencelab-khu.github.io/CLUE-VAD_project/.