通过语言指导和表示对域泛化域的调整来快速解脱纠
IEEE transactions on pattern analysis and machine intelligence
|February 4, 2026
概括
本研究介绍了通过语言指导和表示对齐 (PADG) 的快速解,以改善视觉模型中的域概括 (DG). PADG有效地学习域不变特征,增强模型在未见数据上的性能.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 域泛化 (DG) 旨在通过学习域不变表示来实现在未见域上运行的模型.
- 视觉基础模型 (VFMs) 通过快速调整显示了 DG 的前景,但往往没有专注于解开域不变特征.
研究的目的:
- 通过利用 VFM 的语言提示来增强跨域概括,以解开域不变特征.
- 提出一个新的框架,PADG,用于强大的域泛化.
主要方法:
- 使用大型语言模型 (LLM) 将文本提示解成域不变和域特定组件.
- 采用最坏的显式表示对齐 (WERA) 模块,通过模拟的域位移和表示对齐来增强视觉不变性.
主要成果:
- 在主流总局的基准指标 (PACS,VLCS,OfficeHome,DomainNet,TerraInc) 上,PADG的表现始终优于最先进的方法.
- 在强大的域不变表示学习中表现出有效性.
结论:
- 拟议的PADG框架有效地解决了基于VFM的GD的局限性.
- 通过通过指导语言提示和表示对齐来解开域不变特征来实现卓越的性能.
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