Serial Low-rank Adaptation of Vision Transformer

• URL: http://arxiv.org/abs/2503.17750v1
• Date: Sat, 22 Mar 2025 12:20:02 GMT
• Title: Serial Low-rank Adaptation of Vision Transformer
• Authors: Houqiang Zhong, Shaocheng Shen, Ke Cai, Zhenglong Wu, Jiangchao Yao, Yuan Cheng, Xuefei Li, Xiaoyun Zhang, Li Song, Qiang Hu,
• Abstract summary: Low-rank adaptation (LoRA) is a well-established technique in this domain.<n>We propose Serial LoRA, a novel LoRA variant that introduces a shared low-rank matrix serially composite with the attention mechanism.<n>We conduct extensive experiments on a range of vision foundation models with the transformer structure, and the results confirm consistent superiority of our method.
• Score: 29.30288559885983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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