Outlier Aware Object Detection

Outlier-aware object detection aims to improve the robustness of object detectors by explicitly modeling and handling objects outside the training distribution. Recent research focuses on developing novel methods to better represent the complex relationships between object features, employing techniques like hyperbolic metric learning and normalizing flows to synthesize realistic outlier examples and refine decision boundaries. These advancements are crucial for deploying reliable object detectors in real-world applications like autonomous driving, where encountering unexpected objects is inevitable, and improved accuracy in outlier handling directly translates to increased safety and system reliability.