Integrating Vision and Language: An Improved VAD Model

Automatic anomaly detection in video surveillance is crucial for public and private safety. However, it is challenging because of unclear abnormal events, limited labeled data, and mismatches between different types of data. Traditional video anomaly detection methods mainly focus on spatiotemporal visual features. They often ignore semantic information and interactions between different data types. Additionally, many multimodal approaches use basic fusion methods that do not solve the alignment problems between these types of data. To address these issues, we propose a multimodal framework that includes a Hierarchical Multi-scale Temporal Network (H-MSTN). This network models short-, medium-, and long-term dependencies in visual and textual data. A lightweight cross-modal attention module makes sure the semantics align. Meanwhile, a Multimodal Attention-Based Fusion Transformer (MAFT) refines cross-modal representations in real time. We evaluate this framework using the UCF-Crime and XD-Violence benchmarks. The proposed method achieves 92.42% AUC on UCF-Crime and 88.63% AP on XD-Violence with significantly lower computational cost and faster inference than recent multimodal baselines such as ReFLIP-VAD. These results demonstrate a strong efficiency–accuracy trade-off for real-time deployment while maintaining competitive or improved performance over prior methods such as MVAD and TEVAD.