Out-of-distribution (OOD) detection refers to training the model on an in-distribution (ID) dataset to classify whether the input images come from unknown classes. Considerable effort has been invested in designing various OOD detection methods based on either convolutional neural networks or transformers. However, zero-shot OOD detection methods driven by CLIP, which only require class names for ID, have received less attention. This paper presents a novel method, namely CLIP saying "no" (\textbf{CLIPN}), which empowers the logic of saying "no" within CLIP. Our key motivation is to equip CLIP with the capability of distinguishing OOD and ID samples using positive-semantic prompts and negation-semantic prompts. Specifically, we design a novel learnable "no" prompt and a "no" text encoder to capture negation semantics within images. Subsequently, we introduce two loss functions: the image-text binary-opposite loss and the text semantic-opposite loss, which we use to teach CLIPN to associate images with "no" prompts, thereby enabling it to identify unknown samples. Furthermore, we propose two threshold-free inference algorithms to perform OOD detection by utilizing negation semantics from "no" prompts and the text encoder. Experimental results on 9 benchmark datasets (3 ID datasets and 6 OOD datasets) for the OOD detection task demonstrate that CLIPN, based on ViT-B-16, outperforms 7 well-used algorithms by at least 2.34\% and 11.64\% in terms of AUROC and FPR95 for zero-shot OOD detection on ImageNet-1K. Our CLIPN can serve as a solid foundation for effectively leveraging CLIP in downstream OOD tasks. The code is available on https://github.com/xmed-lab/CLIPN}{https://github.com/xmed-lab/CLIPN.

本文提出了一种名为CLIPN的新方法，通过使用正面语义提示和否定语义提示，赋予CLIP区分OOD和ID样本的能力，从而实现零样本OOD检测。实验结果表明，基于ViT-B-16的CLIPN在ImageNet-1K的零样本OOD检测任务中，至少在AUROC和FPR95两个指标上优于7种常用算法，为有效利用CLIP进行下游OOD任务提供了坚实的基础。

CLIPN用于零猜测异常检测：教导CLIP说不