Sparsity-Controllable Normality Learning With Vision–Language Models for Scenario-Related Video Anomaly Detection

This research presents SCVLM (Sparsity-Controllable Vision-Language Model), a system that detects unusual events in videos by learning what normal behavior looks like from unlabeled data, rather than requiring rare examples of anomalies. The system combines vision (image) and language (text) understanding to identify anomalies as deviations from learned normal patterns, while explaining its decisions in a way that matches human reasoning.