  We present a novel vision-language prompt learning approach for few-shot
out-of-distribution (OOD) detection. Few-shot OOD detection aims to detect OOD
images from classes that are unseen during training using only a few labeled
in-distribution (ID) images. While prompt learning methods such as CoOp have
shown effectiveness and efficiency in few-shot ID classification, they still
face limitations in OOD detection due to the potential presence of
ID-irrelevant information in text embeddings. To address this issue, we
introduce a new approach called Local regularized Context Optimization
(LoCoOp), which performs OOD regularization that utilizes the portions of CLIP
local features as OOD features during training. CLIP's local features have a
lot of ID-irrelevant nuisances (e.g., backgrounds), and by learning to push
them away from the ID class text embeddings, we can remove the nuisances in the
ID class text embeddings and enhance the separation between ID and OOD.
Experiments on the large-scale ImageNet OOD detection benchmarks demonstrate
the superiority of our LoCoOp over zero-shot, fully supervised detection
methods and prompt learning methods. Notably, even in a one-shot setting --
just one label per class, LoCoOp outperforms existing zero-shot and fully
supervised detection methods. The code will be available via
https://github.com/AtsuMiyai/LoCoOp.
