Author

Wen-Jian Zhang

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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

#graph neural networks Open access Sep 2026

A neural-symbolic dynamic graph framework for real-time anomaly detection in software-defined industrial cyber-physical systems

NS-MFM-DGA is introduced, a neural-symbolic multimodal foundation model that incorporates dynamic graph attention for real-time anomaly detection in SD-ICPS and provides a feasible and explainable low-latency anomaly-detection framework for supervisory monitoring in SD-ICPS.

Sen-Lin Jiang, Wen-Jian Zhang, Hao Pan · Engineering Research Express · 0 citations