Spatial–Temporal Decoupled Invertible Neural Networks for Skeleton-Based Video Anomaly Detection

Summary

Researchers propose STD-INN, a skeleton-based video anomaly detection method that separates spatial pose configurations from temporal motion dynamics inside normalizing flow architectures. Spatial and temporal components are modeled by dual independent flows, one using graph convolutional networks and one using causal temporal convolutions, linked by a lightweight cross-calibration module. The authors report state-of-the-art performance on benchmark datasets with lightweight parameters and improved interpretability.