Enhancing Weakly Supervised Anomaly Detection in Surveillance Videos: The CLIP-Augmented Bimodal Memory Enhanced Network

Aiming at the challenges of surveillance video anomaly detection(SVAD),especially the diversity and openness of its event types, we propose CLIP-Augmented Bimodal Memory Enhanced Network for weakly-supervised surveillance video anomaly detection. Specifically, we design a video feature extraction module based on CLIP feature, which significantly improves the ability to capture the semantic content of surveillance videos. Given the problem of semantic diversity of abnormal events, we further design a Bimodal Memory Unit(BMMU), which is used to enhance the model for all types of abnormal events by means of two kinds of memory module, storing the visual features and the textual descriptive features, in order to enhance the model's ability to remember and distinguish various types of anomalous features. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on the UCF-Crime and XD-Violence benchmark datasets.