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 \textbf{Lo}cal regularized \textbf{Co}ntext \textbf{Op}timization (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 \url{https://github.com/AtsuMiyai/LoCoOp}.

我们介绍了一种名为LoCoOp的新方法，它针对few-shot OOD检测进行本地正则化的上下文优化，利用CLIP本地特征的部分作为OOD特征，并展示了它在大规模ImageNet OOD检测基准测试中的优越性。

LoCoOp: 基于Prompt学习的少样本场外检测