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Out-of-distribution (OOD) detection methods often exploit auxiliary outliersto train model identifying OOD samples, especially discovering challengingoutliers from auxiliary outliers dataset to improve OOD detection. However,they may still face limitations in effectively distinguishing between the mostchallenging OOD samples that are much like in-distribution (ID) data, i.e.,ID-like samples. To this end, we propose a novel OOD detection framework thatdiscovers ID-like outliers using CLIP from the vicinity space of the IDsamples, thus helping to identify these most challenging OOD samples. Then aprompt learning framework is proposed that utilizes the identified ID-likeoutliers to further leverage the capabilities of CLIP for OOD detection.Benefiting from the powerful CLIP, we only need a small number of ID samples tolearn the prompts of the model without exposing other auxiliary outlierdatasets. By focusing on the most challenging ID-like OOD samples and elegantlyexploiting the capabilities of CLIP, our method achieves superior few-shotlearning performance on various real-world image datasets (e.g., in 4-shot OODdetection on the ImageNet-1k dataset, our method reduces the average FPR95 by12.16% and improves the average AUROC by 2.76%, compared to state-of-the-artmethods).

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