One Head Eight Arms: Block Matrix based Low Rank Adaptation for CLIP-based Few-Shot Learning

• URL: http://arxiv.org/abs/2501.16720v1
• Date: Tue, 28 Jan 2025 05:54:55 GMT
• Title: One Head Eight Arms: Block Matrix based Low Rank Adaptation for CLIP-based Few-Shot Learning
• Authors: Chunpeng Zhou, Qianqian Shen, Zhi Yu, Jiajun Bu, Haishuai Wang,
• Abstract summary: We propose a novel Block matrix-based low-rank adaptation framework, called Block-LoRA, for fine-tuning Vision-Language Foundation Models.<n>We show that Block-LoRA achieves competitive performance compared to state-of-the-art CLIP-based few-shot methods.
• Score: 11.724194320966959
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advancements in fine-tuning Vision-Language Foundation Models (VLMs) have garnered significant attention for their effectiveness in downstream few-shot learning tasks.While these recent approaches exhibits some performance improvements, they often suffer from excessive training parameters and high computational costs. To address these challenges, we propose a novel Block matrix-based low-rank adaptation framework, called Block-LoRA, for fine-tuning VLMs on downstream few-shot tasks. Inspired by recent work on Low-Rank Adaptation (LoRA), Block-LoRA partitions the original low-rank decomposition matrix of LoRA into a series of sub-matrices while sharing all down-projection sub-matrices. This structure not only reduces the number of training parameters, but also transforms certain complex matrix multiplication operations into simpler matrix addition, significantly lowering the computational cost of fine-tuning. Notably, Block-LoRA enables fine-tuning CLIP on the ImageNet few-shot benchmark using a single 24GB GPU. We also show that Block-LoRA has the more tighter bound of generalization error than vanilla LoRA. Without bells and whistles, extensive experiments demonstrate that Block-LoRA achieves competitive performance compared to state-of-the-art CLIP-based few-shot methods, while maintaining a low training parameters count and reduced computational overhead.

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