Weakly Supervised Video Anomaly Detection

Weakly supervised video anomaly detection (WSVAD) aims to identify unusual events in videos using only video-level labels, avoiding the costly and time-consuming process of frame-level annotation. Current research heavily focuses on leveraging advanced architectures like graph neural networks, transformers, and vision-language models (e.g., CLIP) to extract robust spatio-temporal features and improve anomaly localization, often incorporating techniques like multiple instance learning and self-training. These advancements are significant because they enable the development of more efficient and scalable anomaly detection systems for applications such as surveillance, autonomous driving, and industrial process monitoring. The field is actively exploring methods to mitigate the challenges posed by limited supervision and imbalanced datasets, leading to improved accuracy and robustness.