Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark

• URL: http://arxiv.org/abs/2402.02242v5
• Date: Sun, 29 Jun 2025 17:09:26 GMT
• Title: Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark
• Authors: Yi Xin, Jianjiang Yang, Siqi Luo, Yuntao Du, Qi Qin, Kangrui Cen, Yangfan He, Bin Fu, Xiaokang Yang, Guangtao Zhai, Ming-Hsuan Yang, Xiaohong Liu,
• Abstract summary: Pre-trained vision models (PVMs) have demonstrated remarkable adaptability across a wide range of downstream vision tasks.<n>As these models scale to billions or even trillions of parameters, conventional full fine-tuning has become increasingly impractical due to its high computational and storage demands.<n> parameter-efficient fine-tuning (PEFT) has emerged as a promising alternative, aiming to achieve performance comparable to full fine-tuning while making minimal adjustments to the model parameters.
• Score: 97.8968058408759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained vision models (PVMs) have demonstrated remarkable adaptability across a wide range of downstream vision tasks, showcasing exceptional performance. However, as these models scale to billions or even trillions of parameters, conventional full fine-tuning has become increasingly impractical due to its high computational and storage demands. To address these challenges, parameter-efficient fine-tuning (PEFT) has emerged as a promising alternative, aiming to achieve performance comparable to full fine-tuning while making minimal adjustments to the model parameters. This paper presents a comprehensive survey of the latest advancements in the visual PEFT field, systematically reviewing current methodologies and categorizing them into four primary categories: addition-based, partial-based, unified-based, and multi-task tuning. In addition, this paper offers an in-depth analysis of widely used visual datasets and real-world applications where PEFT methods have been successfully applied. Furthermore, this paper introduces the V-PEFT Bench, a unified benchmark designed to standardize the evaluation of PEFT methods across a diverse set of vision tasks, ensuring consistency and fairness in comparison. Finally, the paper outlines potential directions for future research to propel advances in the PEFT field. A comprehensive collection of resources is available at https://github.com/synbol/Awesome-Parameter-Efficient-Transfer-Learning.

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