Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
Weakly supervised video anomaly detection (WS-VAD) involves identifying the temporal intervals that contain anomalous events in untrimmed videos, where only video-level annotations are provided as supervisory signals. However, a key limitation persists in WS-VAD, as dense frame-level annotations are absent, which often leaves existing methods struggling to learn anomaly semantics effectively. To address this issue, we propose a novel framework named LAS-VAD, short for Learning Anomaly Semantics for WS-VAD, which integrates anomaly-connected component mechanism and intention awareness mechanism. The former is designed to assign video frames into distinct semantic groups within a video, and...
| Authors | Yu Wang, Shengjie Zhao |
|---|---|
| Primary category | cs.CV |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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