ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection

arXiv:2311.15243v3 Announce Type: replace-cross
Abstract: Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection. However, they may still face limitations in effectively distinguishing between the most challenging OOD samples that are much like in-distribution (ID) data, i.e., \idlike samples. To this end, we propose a novel OOD detection framework that discovers \idlike outliers using CLIP \cite{DBLP:conf/icml/RadfordKHRGASAM21} from the vicinity space of the ID …

arxiv cs.ai cs.cv cs.lg detection distribution few-shot prompt prompt learning type