Efficient Hybrid Open-set Segmentation with Synthetic Negative Data

The core message of this article is to propose a novel hybrid anomaly detection approach that combines generative and discriminative cues to efficiently identify unknown visual concepts in dense prediction tasks, such as semantic segmentation. The authors introduce a training setup that leverages synthetic negative data generated by a jointly trained normalizing flow to enable open-set segmentation without relying on real negative samples.