Structured Evidence Selection for Weakly Supervised Video Anomaly Detection
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 evidenc...
| Authors | Chenglizhao Chen, Tianxiang Nan, Wen Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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