Structured Evidence Selection for Weakly Supervised Video Anomaly Detection

• URL: http://arxiv.org/abs/2607.10298v1
• Date: Sat, 11 Jul 2026 13:01:01 GMT
• Title: Structured Evidence Selection for Weakly Supervised Video Anomaly Detection
• Abstract summary: Weakly supervised video anomaly detection relies solely on video-level labels for training.<n>We propose a Structured Evidence Selection framework (SESAD) that reformulates anomaly detection as a structured reasoning process.<n>SESAD reorganizes clip representations into semantically structured candidate evidence and performs context-conditioned selection under scene and action constraints.
• Score: 24.113971346791942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised video anomaly detection relies solely on video-level labels for training, making it difficult to accurately localize anomalous events in complex scenes. In real-world videos, anomalous behaviors exhibit large variations in appearance and temporal duration, while scene appearance and action dynamics are often tightly entangled. Consequently, existing models tend to rely on scene-related statistical cues rather than true behavioral deviations, resulting in unstable detection performance. To address this challenge, we propose a Structured Evidence Selection framework (SESAD) that reformulates anomaly detection as a structured reasoning process over clip-level visual evidence. Instead of directly mapping aggregated features to anomaly scores, SESAD reorganizes clip representations into semantically structured candidate evidence and performs context-conditioned selection under scene and action constraints. This mechanism adaptively emphasizes anomaly-relevant semantics while suppressing scene interference, thereby alleviating semantic entanglement under weak supervision. Furthermore, we introduce a lightweight geometric discrimination module that constructs a dual-prototype structure in the embedding space, enabling anomaly decisions through relative geometric relations. Extensive experiments on UBnormal, ShanghaiTech, and UCF-Crime show that SESAD achieves 67.92, 97.99, and 88.46 AUC, respectively, while maintaining high computational efficiency and overall consistently stable anomaly discrimination.

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