App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 6

Few-shot image classification algorithm based on learning prompts and dual-modal adapters

Liu Fang1
Xu Yifei2
Xie Tianle2
Li Siqi2
1. School of Science, Xi'an Shiyou University, Xi'an 710065, China
2. School of Software Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0409

Abstract

Image classification, as a core task in computer vision, serves as the foundation for image retrieval, object detection, and scene understanding, and plays a crucial role in the implementation of artificial intelligence and industrial upgrading. To address the dependence on manually designed text prompts, limited generalization, and overfitting in few-shot image classification, a COCLIP-Adapter algorithm integrating learnable prompts and dual-modal adapters was developed. Based on CLIP, the algorithm designs a learnable text prompt vector module to generate dynamic text prompts, incorporates a dual-modal adapter to optimize visual and textual features, and employs a multi-task loss function to enhance cross-modal alignment. Experimental results show that on the ImageNet dataset, COCLIP-Adapter improves 1-shot and 16-shot classification accuracy by 39.65% and 10.09% over CLIP, and by up to 1.05%-0.26% over Tip-Adapter-F. On Caltech101, the method achieves 19.14% and 2.61% higher accuracy than CLIP, and up to 0.48% higher than Tip-Adapter-F. These results demonstrate that COCLIP-Adapter effectively enhances few-shot feature learning and improves classification performance under limited data conditions, providing reliable technical support for real-world applications with scarce labeled samples.

Foundation Support

陕西省自然基金基础计划面上项目(2024JC-YBMS-498)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0409
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 6

Publish History

[2026-02-05] Accepted Paper

Cite This Article

刘芳, 徐亦飞, 谢天乐, 等. 基于学习提示和双模态适配器的小样本图像分类算法 [J]. 计算机应用研究, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0409. (Liu Fang, Xu Yifei, Xie Tianle, et al. Few-shot image classification algorithm based on learning prompts and dual-modal adapters [J]. Application Research of Computers, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0409. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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