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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 6

Progcopl: progressive co-prompting learning for vision-language models

Tao Junjie1
Zhang Weifeng1,2
Wang Yuxia3
Miao Yi1
Xu Ling1
1. School of Computer Science & Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou 310000, China
2. School of Information Science & Engineering, Jiaxing University, Jiaxing 314000, China
3. Jiaxing Institute of Metrology & Testing, Jiaxing Zhejiang 314000, China
DOI: 10.19734/j.issn.1001-3695.2024.10.0446

Abstract

The large-scale pre-trained vision-language model CLIP (Contrastive Language-Image Pretraining) aligns images and texts in a shared semantic space, demonstrating robust generalization capabilities across diverse downstream tasks. However, existing prompt learning methods often independently insert learnable prompt vectors into each layer of CLIP’s visual and text encoders. This approach results in limited cross-modal interaction, with independent prompts across layers failing to effectively guide the encoders in capturing task-relevant information. To address these issues, we propose Progressive Co-Prompting Learning (ProgCoPL) . This method introduces text-guided prompt vectors into the visual encoder layers and vision-guided prompt vectors into the text encoder layers, thereby enhancing cross-modal interaction and alignment. Furthermore, ProgCoPL incorporates information transmission channels between prompt vectors across layers, enabling hierarchical and progressive integration of task-specific information. Experiments on 11 datasets show that ProgCoPL efficiently adapts CLIP to downstream tasks, significantly improving its cross-dataset generalization ability. ProgCoPL outperforms existing methods in multiple generalization tests, particularly achieving notable advancements in cross-dataset scenarios.

Foundation Support

中国博士后科学基金资助项目(Grant2022M720569)、浙江省自然科学基金资助项目(GrantLQ21F020022)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0446
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-10] Accepted Paper

Cite This Article

陶俊杰, 张卫锋, 王玉霞, 等. 面向视觉-语言模型的递进互提示学习 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0446. (Tao Junjie, Zhang Weifeng, Wang Yuxia, et al. Progcopl: progressive co-prompting learning for vision-language models [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0446. )

About the Journal

  • 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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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