  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.
