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., ID-like samples. To this end, we propose a novel OOD detection framework that discovers ID-like outliers using CLIP from the vicinity space of the ID samples, thus helping to identify these most challenging OOD samples. Then a prompt learning framework is proposed that utilizes the identified ID-like outliers to further leverage the capabilities of CLIP for OOD detection. Benefiting from the powerful CLIP, we only need a small number of ID samples to learn the prompts of the model without exposing other auxiliary outlier datasets. By focusing on the most challenging ID-like OOD samples and elegantly exploiting the capabilities of CLIP, our method achieves superior few-shot learning performance on various real-world image datasets (e.g., in 4-shot OOD detection on the ImageNet-1k dataset, our method reduces the average FPR95 by 12.16% and improves the average AUROC by 2.76%, compared to state-of-the-art methods).

利用CLIP在与ID样本相邻的空间中发现ID样本的异常值从而帮助检测最具挑战性的OOD样本，进而利用这些异常值进一步提升OOD检测中CLIP的效能，在各种真实世界图像数据集上表现出卓越的少样本学习性能（例如，在ImageNet-1k数据集上的4样本OOD检测中，与现有最先进方法相比，我们的方法将平均FPR95降低了12.16％，平均AUROC提高了2.76％）。

少样本外分布检测的类ID式提示学习