Caifeng Shan

Reconstructive Visual Tuning for Weakly Supervised Video Anomaly Detection

Weakly supervised video anomaly detection (WS-VAD) presents a significant challenge in security video surveillance, as it aims to accurately identify anomaly frames in untrimmed videos with only video-level supervision. Several recent studies exploit vision-language pre-training models, e.g., CLIP, to take advantage of...