2025
FUVAS: Few-shot Unsupervised Video Anomaly Segmentation via Low-Rank Factorization of Spatio-Temporal Features
ICASSP 2025accepted
Video anomaly detection (VAD) methods analyze untrimmed videos to make temporal decisions at the frame level to identify abnormal events. An important challenge of VAD approaches is the accurate spatial segmentation of the anomalous regions within frames to provide interpretability of anomalies. In…