Open access
2026
Multimodal DynaST: A Multimodal Framework With Dynamic Sample Selection and Temporal Context Aggregation for Weakly Supervised Video Anomaly Detection
Experimental results on UCF-Crime, ShanghaiTech, and XD-Violence demonstrate the effectiveness of multimodal DynaST, a multimodal framework for weakly supervised video anomaly detection, and the benefit of multimodal learning for robust anomaly detection.
Wen-Jian Zhang, Xin-Xin Fan, Qi-Wei Pan et al.
· IEEE Access · 0 citations