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 this https URL

本研究针对深度学习模型中的未知分布（OOD）检测问题，提出了一种基于文本 - 图像预训练模型的一类开放集合 OOD 检测器，可以检测细粒度或粗粒度标签，甚至自然语言定义的各种 OOD，证实本方法在所有测试项目中表现出卓越的性能。