CVPR 2023 - Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection

In this episode we discuss Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection by Nishant Kumar, Siniša Šegvić, Abouzar Eslami, Stefan Gumhold. The paper proposes a novel outlier-aware object detection framework that improves on existing approaches by learning the joint data distribution of all inlier classes with an invertible normalizing flow. This ensures that the synthesized outliers have a lower likelihood than inliers from all object classes, resulting in a better decision boundary between inlier and outlier objects. The approach shows significant improvement over the state-of-the-art for outlier-aware object detection on both image and video datasets, making it a promising solution for real-world deployment of reliable object detectors.