SimLabel：基于预训练视觉-语言模型的一致性引导的分布外检测
SimLabel: Consistency-Guided OOD Detection with Pretrained Vision-Language Models
AI总结：
提出SimLabel，一种利用跨类别语义一致性改进图像-类别相似度度量的后处理策略，增强预训练视觉-语言模型的零样本OOD检测性能。
摘要：
在现实世界的机器学习应用中，尤其是在安全关键的领域，检测分布外（OOD）数据至关重要。现有方法通常利用视觉-语言模型（VLMs）中的语言信息，通过丰富的类别级文本信息改进置信度估计，从而增强OOD检测。然而，当基于分布内（ID）文本-图像亲和力构建OOD检测分数时，现有工作要么关注每个ID类别，要么关注整个ID标签集，忽视了ID类别之间的内在联系。我们发现，不同ID类别之间的语义信息对有效的OOD检测是有益的。因此，我们研究了VLM中不同语义相关ID标签之间的图像-文本理解能力，并提出了一种新颖的事后策略，称为SimLabel。SimLabel通过建立一种更稳健的图像-类别相似度度量，该度量考虑了一组相似类别标签上的一致性，从而增强了ID和OOD样本之间的可分离性。大量实验证明了SimLabel在各种零样本OOD检测基准上的优越性能。所提出的模型还扩展到各种VLM骨干网络，展示了其良好的泛化能力。我们的演示和实现代码可在以下网址获取：https://github.com/ShuZou-1/SimLabel。
英文摘要：
Detecting out-of-distribution (OOD) data is crucial in real-world machine learning applications, particularly in safety-critical domains. Existing methods often leverage language information from vision-language models (VLMs) to enhance OOD detection by improving confidence estimation through rich class-wise text information. However, when building OOD detection score upon on in-distribution (ID) text-image affinity, existing works either focus on each ID class or whole ID label sets, overlooking inherent ID classes' connection. We find that the semantic information across different ID classes is beneficial for effective OOD detection. We thus investigate the ability of image-text comprehension among different semantic-related ID labels in VLMs and propose a novel post-hoc strategy called SimLabel. SimLabel enhances the separability between ID and OOD samples by establishing a more robust image-class similarity metric that considers consistency over a set of similar class labels. Extensive experiments demonstrate the superior performance of SimLabel on various zero-shot OOD detection benchmarks. The proposed model is also extended to various VLM-backbones, demonstrating its good generalization ability. Our demonstration and implementation codes are available at: https://github.com/ShuZou-1/SimLabel.
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。
