  Recognizing out-of-distribution (OOD) samples is critical for machine
learning systems deployed in the open world. The vast majority of OOD detection
methods are driven by a single modality (e.g., either vision or language),
leaving the rich information in multi-modal representations untapped. Inspired
by the recent success of vision-language pre-training, this paper enriches the
landscape of OOD detection from a single-modal to a multi-modal regime.
Particularly, we propose Maximum Concept Matching (MCM), a simple yet effective
zero-shot OOD detection method based on aligning visual features with textual
concepts. We contribute in-depth analysis and theoretical insights to
understand the effectiveness of MCM. Extensive experiments demonstrate that MCM
achieves superior performance on a wide variety of real-world tasks. MCM with
vision-language features outperforms a common baseline with pure visual
features on a hard OOD task with semantically similar classes by 13.1% (AUROC).
Code is available at https://github.com/deeplearning-wisc/MCM.
