Learning Suspected Anomalies from Event Prompts for Video Anomaly Detection

The author proposes a novel framework to guide the learning of suspected anomalies from event prompts, enhancing weakly supervised video anomaly detection. By utilizing semantic anomaly similarity and multi-prompt learning, the model outperforms state-of-the-art methods in various datasets.