ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions

ArXi:2604.07772v1 Announce Type: new Open-world video anomaly detection (OWVAD) aims to detect and explain abnormal events under different anomaly definitions, which is important for applications such as intelligent surveillance and live-streaming content moderation. Recent MLLM-based methods have shown promising open-world generalization, but still suffer from three major limitations: inefficiency for practical deployment, lack of streaming processing adaptation, and limited for dynamic anomaly definitions in both modeling and evaluation.