基于预训练视觉-语言模型的负标签引导OOD检测
Negative Label Guided OOD Detection with Pretrained Vision-Language Models
AI总结：
本文提出NegLabel方法，利用大规模语料库中的负标签增强预训练视觉-语言模型的OOD检测能力，在多个基准上取得最先进性能并具备良好的泛化性与鲁棒性。
摘要：
分布外（OOD）检测旨在识别来自未知类别的样本，在可信模型应对意外输入错误方面发挥着关键作用。大量研究致力于探索视觉模态下的OOD检测。视觉-语言模型（VLMs）能够利用文本和视觉信息进行各种多模态应用，但很少有OOD检测方法考虑文本模态的信息。在本文中，我们提出了一种新颖的事后OOD检测方法，称为NegLabel，该方法从大规模语料库中获取大量负标签。我们设计了一种与负标签协同工作的新颖OOD分数方案。理论分析有助于理解负标签的机制。大量实验表明，我们的方法NegLabel在各种OOD检测基准上达到了最先进的性能，并在多种VLM架构上具有良好的泛化能力。此外，我们的方法NegLabel对多样化的领域偏移表现出显著的鲁棒性。代码可在 https://github.com/tmlr-group/NegLabel 获取。
英文摘要：
Out-of-distribution (OOD) detection aims at identifying samples from unknown classes, playing a crucial role in trustworthy models against errors on unexpected inputs. Extensive research has been dedicated to exploring OOD detection in the vision modality. Vision-language models (VLMs) can leverage both textual and visual information for various multi-modal applications, whereas few OOD detection methods take into account information from the text modality. In this paper, we propose a novel post hoc OOD detection method, called NegLabel, which takes a vast number of negative labels from extensive corpus databases. We design a novel scheme for the OOD score collaborated with negative labels. Theoretical analysis helps to understand the mechanism of negative labels. Extensive experiments demonstrate that our method NegLabel achieves state-of-the-art performance on various OOD detection benchmarks and generalizes well on multiple VLM architectures. Furthermore, our method NegLabel exhibits remarkable robustness against diverse domain shifts. The codes are available at https://github.com/tmlr-group/NegLabel.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
