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针对中国非物质文化遗产美术作品分类中处理效率低、数据复杂等问题，提出了一种基于预训练视觉语言大模型的上下文提示微调策略，以提升小样本情况下的分类性能并应对当前任务的挑战。该方法通过引入可学习的上下文优化提示(软提示),使模型能够在少量样本条件下快速适应下游分类任务，从而有效缩短训练时间并提升收敛速度。具体而言，利用注意力机制，将由软提示生成的文本特征与预训练视觉语言模型的原始特征相结合，并通过对比损失优化嵌入表示。这一机制减少了不同特征之间的嵌入差异，避免了模型对已知类别的过度拟合，提升了在未见类别上的泛化能力。此外，保留原始特征信息帮助模型避免训练过程中遗忘基础知识，确保即便在小样本条件下，模型仍能保持较高的分类准确率。实验结果表明，所提出方法在非遗美术图像分类任务中的准确率提升了1.79%,泛化识别能力提升了10.4%,同时具备较低的计算成本。
Abstract：To address the issues of prolonged processing time, low efficiency, and high data complexity in the classification of Chinese intangible cultural heritage(ICH) artworks, this paper proposes a context-based text prompt tuning strategy based on a pre-trained vision-language model. This approach introduces trainable context optimization soft prompts, enabling the model to quickly adapt to downstream classification tasks under limited sample conditions, thereby effectively reducing training time and improving convergence speed. Specifically, the proposed method integrates text features generated by the soft prompts with the original features of the pre-trained vision-language model through an attention mechanism, and optimizes the embedded representations via a contrastive loss function. This mechanism significantly reduces the embedding discrepancy between the two types of features, preventing the model from overfitting to visible base categories and enhancing its generalization ability to unseen classes. Moreover, the retention of original features helps mitigate catastrophic forgetting during training, ensuring high classification accuracy even under few-shot conditions. Experimental results demonstrate that the proposed method improves classification accuracy by 1.79%, enhances generalization by 10.4%, and maintains low computational cost.
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基本信息:
DOI：10.16152/j.cnki.xdxbzr.2025-01-009
中图分类号:J05;TP391.41
引用信息:
[1]张秦瑜,刘鑫达,鲁倬铭,等.面向非遗美术图像分类的提示学习方法[J].西北大学学报(自然科学版),2025,55(01):106-117.DOI:10.16152/j.cnki.xdxbzr.2025-01-009.
基金信息:
虚拟现实技术与系统全国重点实验室(北京航空航天大学)开放课题基金(VRLAB2024C02); 文化和旅游部重点实验室项目(1222000812、cr2021K01); 西安市科技计划社会发展科技创新示范项目(2024JH-CXSF-0014); 国家自然科学基金(62271393)
2025-01-20
2025-01-20
2025-01-20