Large-scale pre-trained vision models (PVMs) have shown great potential for
adaptability across various downstream vision tasks. However, with
state-of-the-art PVMs growing to billions or even trillions of parameters, the
standard full fine-tuning paradigm is becoming unsustainable due to high
computational and storage demands. In response, researchers are exploring
parameter-efficient fine-tuning (PEFT), which seeks to exceed the performance
of full fine-tuning with minimal parameter modifications. This survey provides
a comprehensive overview and future directions for visual PEFT, offering a
systematic review of the latest advancements. First, we provide a formal
definition of PEFT and discuss model pre-training methods. We then categorize
existing methods into three categories: addition-based, partial-based, and
unified-based. Finally, we introduce the commonly used datasets and
applications and suggest potential future research challenges. A comprehensive
collection of resources is available at
this https URL

规模化预训练视觉模型（PVMs）在各种下游视觉任务中表现出很大的适应性。然而，随着最先进的 PVMs 达到数十亿甚至数万亿参数，传统的完全微调范式变得难以持续，因为其需要巨大的计算和存储需求。为了应对这一挑战，研究人员正在探索参数高效的微调（PEFT），旨在通过最小的参数修改超越完全微调的性能。本调查提供了对视觉 PEFT 的综合概述和未来方向，对最新的进展进行了系统回顾。首先，我们提供了 PEFT 的正式定义，并讨论了模型预训练方法。然后，我们将现有方法分为三类：基于添加的、基于部分的和基于统一的。最后，我们介绍了常用的数据集和应用，并提出了未来研究的潜在挑战。所有相关资源可以在该链接中找到。