Segment Every Out-of-Distribution Object

arXiv:2311.16516v4 Announce Type: replace
Abstract: Semantic segmentation models, while effective for in-distribution categories, face challenges in real-world deployment due to encountering out-of-distribution (OoD) objects. Detecting these OoD objects is crucial for safety-critical applications. Existing methods rely on anomaly scores, but choosing a suitable threshold for generating masks presents difficulties and can lead to fragmentation and inaccuracy. This paper introduces a method to convert anomaly \textbf{S}core \textbf{T}o segmentation \textbf{M}ask, called S2M, a simple and effective framework for OoD detection in semantic …

abstract anomaly applications arxiv challenges cs.cv deployment distribution every face fragmentation masks object objects safety safety-critical segment segmentation semantic threshold type world