Structured Evidence Selection for Weakly Supervised Video Anomaly Detection
A Structured Evidence Selection framework (SESAD) that reformulates anomaly detection as a structured reasoning process over clip-level visual evidence and performs context-conditioned selection under scene and action constraints, thereby alleviating semantic entanglement under weak supervision.