  Detection of out-of-distribution (OOD) samples is crucial for safe real-world
deployment of machine learning models. Recent advances in vision language
foundation models have made them capable of detecting OOD samples without
requiring in-distribution (ID) images. However, these zero-shot methods often
underperform as they do not adequately consider ID class likelihoods in their
detection confidence scoring. Hence, we introduce CLIPScope, a zero-shot OOD
detection approach that normalizes the confidence score of a sample by class
likelihoods, akin to a Bayesian posterior update. Furthermore, CLIPScope
incorporates a novel strategy to mine OOD classes from a large lexical
database. It selects class labels that are farthest and nearest to ID classes
in terms of CLIP embedding distance to maximize coverage of OOD samples. We
conduct extensive ablation studies and empirical evaluations, demonstrating
state of the art performance of CLIPScope across various OOD detection
benchmarks.
