Fine-tuning large pre-trained vision foundation models in a parameter-efficient manner is critical for downstream vision tasks, considering the practical constraints of computational and storage costs. Low-rank adaptation (LoRA) is a well-established technique in this domain, achieving impressive efficiency by reducing the parameter space to a low-rank form. However, developing more advanced low-rank adaptation methods to reduce parameters and memory requirements remains a significant challenge in resource-constrained application scenarios. In this study, we consider on top of the commonly used vision transformer and propose Serial LoRA, a novel LoRA variant that introduces a shared low-rank matrix serially composite with the attention mechanism. Such a design extracts the underlying commonality of parameters in adaptation, significantly reducing redundancy. Notably, Serial LoRA uses only 1/4 parameters of LoRA but achieves comparable performance in most cases. We conduct extensive experiments on a range of vision foundation models with the transformer structure, and the results confirm consistent superiority of our method.
View on arXiv@article{zhong2025_2503.17750,
title={ Serial Low-rank Adaptation of Vision Transformer },
author={ Houqiang Zhong and Shaocheng Shen and Ke Cai and Zhenglong Wu and Jiangchao Yao and Yuan Cheng and Xuefei Li and Xiaoyun Zhang and Li Song and Qiang Hu },
journal={arXiv preprint arXiv:2503.17750},
year={ 2025 }
}