---
title: "Domain Prompt Learning with Quaternion Networks (Extended Abstract)"
authors:
  - "Qinglong Cao"
  - "Zhengqin Xu"
  - "Yuntian Chen"
  - "Chao Ma"
  - "Xiaokang Yang"
doi: "10.24963/ijcai.2025/1209"
url: "https://ijcai.org/proceedings/2025/1209"
pdf: "https://ijcai.org/proceedings/2025/1209.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10881-10884"
track: "Sister Conferences Best Papers"
keywords:
  - "Sister Conferences Best Papers: Computer Vision"
  - "Sister Conferences Best Papers: Multidisciplinary Topics and Applications"
  - "Sister Conferences Best Papers: Machine Learning"
---

# Domain Prompt Learning with Quaternion Networks (Extended Abstract)

Qinglong Cao, Zhengqin Xu, Yuntian Chen, Chao Ma, Xiaokang Yang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10881-10884. DOI: [10.24963/ijcai.2025/1209](https://doi.org/10.24963/ijcai.2025/1209)

## Abstract

Foundational vision-language models (VLMs) like CLIP have revolutionized image recognition, but adapting them to specialized domains with limited data remains challenging. We propose Domain Prompt Learning with Quaternion Networks (DPLQ), which leverages domain-specific foundation models and quaternion-based prompt tuning to effectively transfer recognition capabilities. Our method achieves state-of-the-art results in remote sensing and medical imaging tasks. This extended abstract highlights the key contributions and performance of DPLQ.