无需标签的视觉-语言模型适配：综合综述
Adapting Vision-Language Models Without Labels: A Comprehensive Survey
AI总结：
本文提出了一种基于无标注视觉数据可用性及性质的分类法，将无监督视觉-语言模型适配方法分为无数据迁移、无监督域迁移、情景式测试时适配和在线测试时适配四类，并系统分析了各范式的方法、基准与未来方向。
摘要：
视觉-语言模型（VLMs）在广泛的任务中展现出了显著的泛化能力。然而，当直接应用于特定下游场景而未进行任务特定适配时，其性能往往仍非最优。为了在保持数据效率的同时提升其效用，近期研究日益聚焦于不依赖标注数据的无监督适配方法。尽管该领域兴趣日益增长，但针对无监督VLM适配的统一、面向任务的综述仍显匮乏。为填补这一空白，我们对该领域进行了全面且结构化的概述。我们基于无标注视觉数据的可用性及其性质提出了一种分类法，将现有方法归为四大关键范式：无数据迁移（无数据）、无监督域迁移（丰富数据）、情景式测试时适配（批量数据）以及在线测试时适配（流式数据）。在此框架内，我们分析了每种范式下的核心方法论与适配策略，旨在建立对该领域的系统性理解。此外，我们回顾了跨多种应用的代表性基准，并强调了开放挑战及未来研究的有前景方向。一个持续维护的相关文献仓库可在 https://github.com/tim-learn/Awesome-LabelFree-VLMs 获取。
英文摘要：
Vision-Language Models (VLMs) have demonstrated remarkable generalization capabilities across a wide range of tasks. However, their performance often remains suboptimal when directly applied to specific downstream scenarios without task-specific adaptation. To enhance their utility while preserving data efficiency, recent research has increasingly focused on unsupervised adaptation methods that do not rely on labeled data. Despite the growing interest in this area, there remains a lack of a unified, task-oriented survey dedicated to unsupervised VLM adaptation. To bridge this gap, we present a comprehensive and structured overview of the field. We propose a taxonomy based on the availability and nature of unlabeled visual data, categorizing existing approaches into four key paradigms: Data-Free Transfer (no data), Unsupervised Domain Transfer (abundant data), Episodic Test-Time Adaptation (batch data), and Online Test-Time Adaptation (streaming data). Within this framework, we analyze core methodologies and adaptation strategies associated with each paradigm, aiming to establish a systematic understanding of the field. Additionally, we review representative benchmarks across diverse applications and highlight open challenges and promising directions for future research. An actively maintained repository of relevant literature is available at https://github.com/tim-learn/Awesome-LabelFree-VLMs.
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。
