Out-of-distribution (OOD) detection is a critical task to ensure the
reliability and security of machine learning models deployed in real-world
applications. Conventional methods for OOD detection that rely on single-modal
information, often struggle to capture the rich variety of OOD instances. The
primary difficulty in OOD detection arises when an input image has numerous
similarities to a particular class in the in-distribution (ID) dataset, e.g.,
wolf to dog, causing the model to misclassify it. Nevertheless, it may be easy
to distinguish these classes in the semantic domain. To this end, in this
paper, a novel method called ODPC is proposed, in which specific prompts to
generate OOD peer classes of ID semantics are designed by a large language
model as an auxiliary modality to facilitate detection. Moreover, a contrastive
loss based on OOD peer classes is devised to learn compact representations of
ID classes and improve the clarity of boundaries between different classes. The
extensive experiments on five benchmark datasets show that the method we
propose can yield state-of-the-art results.

提出了一种名为 ODPC 的新方法，通过大型语言模型设计生成特定提示词来产生具有 ID 语义的 OOD 对等类，以便于检测，并采用基于 OOD 对等类的对比损失来学习紧凑的 ID 类别表示，并改善不同类别之间的界限清晰度。在五个基准数据集上进行的广泛实验表明，该方法可以得到最先进的结果。