通过提示驱动适配器提升无源域泛化性能
PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization
- 用提示驱动的文本适配器捕捉风格特征分布
- 引入重采样策略覆盖更全面的领域知识
- 适合需要高效适应未知领域的视觉模型研究者
无源域泛化（SFDG）旨在不访问源域数据的情况下，使模型适应未见的目标域。现有方法主要依赖视觉语言模型（如CLIP）的文本模态，通过提取多样风格特征或从领域库中学习统一的文本表征。然而，风格特征和领域库难以充分捕捉完整的领域知识。本文提出提示驱动文本适配器（PromptTA），通过更好建模风格特征分布并采用重采样策略，确保领域知识的全面覆盖。进一步设计文本适配器，从这些风格特征中学习，实现高效领域信息存储。在四个基准数据集上的大量实验表明，PromptTA达到当前最优性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。