  We focus on the challenge of out-of-distribution (OOD) detection in deep
learning models, a crucial aspect in ensuring reliability. Despite considerable
effort, the problem remains significantly challenging in deep learning models
due to their propensity to output over-confident predictions for OOD inputs. We
propose a novel one-class open-set OOD detector that leverages text-image
pre-trained models in a zero-shot fashion and incorporates various descriptions
of in-domain and OOD. Our approach is designed to detect anything not in-domain
and offers the flexibility to detect a wide variety of OOD, defined via fine-
or coarse-grained labels, or even in natural language. We evaluate our approach
on challenging benchmarks including large-scale datasets containing
fine-grained, semantically similar classes, distributionally shifted images,
and multi-object images containing a mixture of in-domain and OOD objects. Our
method shows superior performance over previous methods on all benchmarks. Code
is available at https://github.com/gyhandy/One-Class-Anything
