The paper proposes a novel out-of-distribution (OOD) detection method called NegPrompt that leverages pre-trained vision-language models (VLMs) like CLIP. The key idea is to learn a set of negative prompts, each representing a negative connotation of a given in-distribution (ID) class label, to delineate the boundaries between ID and OOD images.
The main highlights are:
NegPrompt learns the negative prompts using only the ID training data, without relying on any external outlier data, addressing the mismatch between OOD images and ID categories that plagues existing prompt learning-based OOD detection methods.
The learned negative prompts are transferable to novel class labels, enabling NegPrompt to work in open-vocabulary learning scenarios where the inference stage can contain ID classes not present during training.
Extensive experiments on ImageNet-based benchmarks show that NegPrompt consistently outperforms state-of-the-art prompt learning-based OOD detection methods in both conventional and hard OOD detection settings.
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by Tianqi Li,Gu... at arxiv.org 04-05-2024
https://arxiv.org/pdf/2404.03248.pdfDeeper Inquiries