視覺語言模型的奇異值少樣本適應

Singular Value Few-shot Adaptation of Vision-Language Models

September 3, 2025

作者: Taha Koleilat, Hassan Rivaz, Yiming Xiao

cs.AI

摘要

如CLIP等视觉-语言模型（VLMs）已在多种应用中展现了卓越的零样本与少样本学习能力。然而，由于对提示工程的依赖及全模型微调的高昂成本，将这些模型适应于新的细粒度领域仍具挑战。现有适应方法依赖于增强组件，如提示令牌与适配器模块，这可能会限制适应质量，导致模型不稳定，并损害预训练期间习得的丰富知识。本研究提出CLIP-SVD，一种创新的多模态且参数高效的适应技术，它利用奇异值分解（SVD）在不引入额外模块的情况下调整CLIP内部参数空间。具体而言，我们仅微调CLIP参数矩阵的奇异值，以重新缩放基向量实现领域适应，同时保留预训练模型。这一设计仅需模型总参数的0.04%，即可提升适应性能，并更好地保持其泛化能力。CLIP-SVD在11个自然领域和10个生物医学数据集上取得了最先进的分类结果，在少样本设置下的准确率与泛化性均优于先前方法。此外，我们采用基于自然语言的方法分析CLIP适应的有效性与动态性，以实现CLIP-SVD的可解释性。代码已公开于https://github.com/HealthX-Lab/CLIP-SVD。

English

Vision-language models (VLMs) like CLIP have shown impressive zero-shot and few-shot learning capabilities across diverse applications. However, adapting these models to new fine-grained domains remains difficult due to reliance on prompt engineering and the high cost of full model fine-tuning. Existing adaptation approaches rely on augmented components, such as prompt tokens and adapter modules, which could limit adaptation quality, destabilize the model, and compromise the rich knowledge learned during pretraining. In this work, we present CLIP-SVD, a novel multi-modal and parameter-efficient adaptation technique that leverages Singular Value Decomposition (SVD) to modify the internal parameter space of CLIP without injecting additional modules. Specifically, we fine-tune only the singular values of the CLIP parameter matrices to rescale the basis vectors for domain adaptation while retaining the pretrained model. This design enables enhanced adaptation performance using only 0.04\% of the model's total parameters and better preservation of its generalization ability. CLIP-SVD achieves state-of-the-art classification results on 11 natural and 10 biomedical datasets, outperforming previous methods in both accuracy and generalization under few-shot settings. Additionally, we leverage a natural language-based approach to analyze the effectiveness and dynamics of the CLIP adaptation to allow interpretability of CLIP-SVD. The code is publicly available at https://github.com/HealthX-Lab/CLIP-SVD.