Detecting out-of-distribution (OOD) samples is essential when deploying machine learning models in open-world scenarios. Zero-shot OOD detection, requiring no training on in-distribution (ID) data, has been possible with the advent of vision-language models like CLIP. Existing methods build a text-based classifier with only closed-set labels. However, this largely restricts the inherent capability of CLIP to recognize samples from large and open label space. In this paper, we propose to tackle this constraint by leveraging the expert knowledge and reasoning capability of large language models (LLM) to Envision potential Outlier Exposure, termed EOE, without access to any actual OOD data. Owing to better adaptation to open-world scenarios, EOE can be generalized to different tasks, including far, near, and fine-grained OOD detection. Technically, we design (1) LLM prompts based on visual similarity to generate potential outlier class labels specialized for OOD detection, as well as (2) a new score function based on potential outlier penalty to distinguish hard OOD samples effectively. Empirically, EOE achieves state-of-the-art performance across different OOD tasks and can be effectively scaled to the ImageNet-1K dataset. The code is publicly available at: https://github.com/tmlr-group/EOE.

通过利用大型语言模型的专业知识和推理能力，我们提出了一种名为“Envision potential Outlier Exposure”（EOE）的方法，可以在没有访问任何真实OOD数据的情况下，通过视觉相似性生成潜在异常类标签并设计有效的分数函数来区分困难的OOD样本，从而实现在不同OOD任务上的最先进性能，并能扩展到ImageNet-1K数据集。

利用大型语言模型对异常样本曝光进行预测以进行离群检测