Visual Attribute based Prompt Learning Method for Fine-grained Ship Recognition[J]. Chinese Journal of Ship Research. DOI: 10.19693/j.issn.1673-3185.04407
Citation: Visual Attribute based Prompt Learning Method for Fine-grained Ship Recognition[J]. Chinese Journal of Ship Research. DOI: 10.19693/j.issn.1673-3185.04407

Visual Attribute based Prompt Learning Method for Fine-grained Ship Recognition

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  • Received Date: March 12, 2025
  • Official website online publication date: May 22, 2025
© 2025 The Authors. Published by Editorial Office of Chinese Journal of Ship Research. Creative Commons License
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • [Objective]Ship image recognition technology plays a significant role in marine domain. A Visual Attribute Prompt Learning (VAPT) mechanism is proposed in this study to address the challenges of strong interference, data scarcity, and inadequate semantic feature modeling encountered in deep learning methods for ship image recognition tasks. [Methods]The framework establishes a large-scale pre-trained visual attribute codebook and incorporates a Multi-head Cross-Attention Mechanism (MCA) to achieve attribute matching and selection procedures, enabling effective alignment with deep visual models to enhance their capability in identifying critical ship features. [Results]Experimental results on a self-constructed dataset containing approximately 50,000 ship images demonstrates that the proposed method achieves approximately 4% accuracy improvement compared to baseline models. [Conclusion]The research outcomes provide a novel low-cost technical pathway for feature decoupling and knowledge transfer in target recognition tasks under complex marine environments, offering significant implications for intelligent maritime monitoring systems.
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