  Few-shot OOD detection focuses on recognizing out-of-distribution (OOD)
images that belong to classes unseen during training, with the use of only a
small number of labeled in-distribution (ID) images. Up to now, a mainstream
strategy is based on large-scale vision-language models, such as CLIP. However,
these methods overlook a crucial issue: the lack of reliable OOD supervision
information, which can lead to biased boundaries between in-distribution (ID)
and OOD. To tackle this problem, we propose CLIP-driven Outliers
Synthesis~(CLIP-OS). Firstly, CLIP-OS enhances patch-level features' perception
by newly proposed patch uniform convolution, and adaptively obtains the
proportion of ID-relevant information by employing CLIP-surgery-discrepancy,
thus achieving separation between ID-relevant and ID-irrelevant. Next, CLIP-OS
synthesizes reliable OOD data by mixing up ID-relevant features from different
classes to provide OOD supervision information. Afterward, CLIP-OS leverages
synthetic OOD samples by unknown-aware prompt learning to enhance the
separability of ID and OOD. Extensive experiments across multiple benchmarks
demonstrate that CLIP-OS achieves superior few-shot OOD detection capability.
