基于CLIP的域名通用化和域名适应:一个全面的调查
IEEE transactions on pattern analysis and machine intelligence
|January 12, 2026
概括
本调查提供了对对比语言图像预训练 (CLIP) 在域泛化 (DG) 和域适应 (DA) 应用的全面概述. 它对方法进行了分类,分析了趋势,并讨论了强大的AI系统面临的挑战.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 域泛化 (DG) 和域适应 (DA) 对在不同环境中人工智能模型的稳定性至关重要.
- 对比的语言图像预训 (CLIP) 为未见的领域提供了强大的零射击能力.
- 需要对CLIP在 DG 和 DA 中的作用进行系统审查.
研究的目的:
- 为 CLIP 驱动的 DG 和 DA 提供统一而深入的概述.
- 为 DG/DA 场景建立一个分类法 (来源可访问性,编号,标签关系).
- 综合现有研究并确定研究缺口.
主要方法:
- 总局方法的分类:快速优化和CLIP作为骨干架构.
- 检查DA方法:可用源和无源,专注于知识转移.
- 综合分析了 DG 和 DA 的趋势,确定了模式和原则.
主要成果:
- 通过提供可转移的功能和零射击能力,CLIP显著增强了DG和DA.
- 一个结构化的分类学有助于理解各种各样的DG/DA场景.
- 趋势分析揭示了方法论原则和情景依赖的行为.
结论:
- 本调查综合了基于CLIP的DG/DA研究,为从业者提供了见解.
- 识别诸如部署,LLM集成,可解释性和灾难性遗忘等挑战.
- 概述了基于CLIP的可扩展和可靠的DG/DA系统的未来方向.
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