Huanhuan Liang

ClimaOoD: Improving Anomaly Segmentation via Physically Realistic Synthetic Data

Anomaly segmentation seeks to detect and localize unknown or out-of-distribution (OoD) objects that fall outside predefined semantic classes--a capability essential for safe autonomous driving. However, the scarcity and limited diversity of anomaly data severely constrain model generalization in ope