PromptTA：用于无源域泛化的提示驱动文本适配器
PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization
AI总结：
本文提出PromptTA方法，通过提示驱动文本适配器更好地捕捉风格特征分布并采用重采样确保域知识全面覆盖，在无源域泛化任务中实现了最先进的性能。
摘要：
无源域泛化（SFDG）致力于解决在无法访问源域数据的情况下，将模型适配到未见目标域的挑战。为了应对这一艰巨任务，近期SFDG的进展主要集中在利用如CLIP等视觉语言模型的文本模态。这些方法包括基于从文本和学习到的提示中提取的多样风格特征来开发可迁移的线性分类器，或从域库中推导域统一的文本表示。然而，风格特征和域库在捕捉全面的域知识方面均存在局限性。在本研究中，我们提出了提示驱动文本适配器（PromptTA）方法，旨在更好地捕捉风格特征的分布，并采用重采样以确保对域知识的全面覆盖。为了进一步利用这些丰富的域信息，我们引入了一种文本适配器，该适配器从这些风格特征中学习以实现高效的域信息存储。在四个基准数据集上进行的大量实验表明，PromptTA实现了最先进的性能。代码可在https://github.com/zhanghr2001/PromptTA获取。
英文摘要：
Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. To deal with this challenging task, recent advances in SFDG have primarily focused on leveraging the text modality of vision-language models such as CLIP. These methods involve developing a transferable linear classifier based on diverse style features extracted from the text and learned prompts or deriving domain-unified text representations from domain banks. However, both style features and domain banks have limitations in capturing comprehensive domain knowledge. In this work, we propose Prompt-Driven Text Adapter (PromptTA) method, which is designed to better capture the distribution of style features and employ resampling to ensure thorough coverage of domain knowledge. To further leverage this rich domain information, we introduce a text adapter that learns from these style features for efficient domain information storage. Extensive experiments conducted on four benchmark datasets demonstrate that PromptTA achieves state-of-the-art performance. The code is available at https://github.com/zhanghr2001/PromptTA.
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。
