Journal of Engineering, Project, and Production Management, 2027, 17(1), 2026-19
Cross-Modal Retrieval in Art Design via Attribute Fusion and Multi-Task Collaborative Learning
Lecturer, Arts Department, Zhengzhou Railway Vocational and Technical University, Zhengzhou, 450018, China, E-mail: guoyanan10@outlook.com
Project Management
Received January 7, 2026; revised March 9, 2026; September 14, 2026; accepted September 24, 2026
Available online October 26, 2026
Abstract: Existing cross-modal retrieval ignores key attribute features such as genre and author in art design, and independent encoding of images and texts leads to poor semantic alignment. Therefore, this study proposes an art design cross-modal retrieval model that combines attribute deep fusion and multi-task collaborative learning. This method first employs a prompt-template-based strategy to convert structured attribute information into natural-language sentences. From a causal inference perspective, separate encoding paths are designed to integrate attributes, text, and visual features in depth. In addition, the model introduces a multi-task collaborative learning framework to learn more robust shared representations by jointly optimizing cross-modal retrieval and artistic attribute classification. Experimental results on the Rijksmuseum data set showed that, in the text-retrieval image task, the average precision of the cross-modal retrieval model reached 62.7%, and the total recall rate reached 397.8%. In the image-retrieval text task, the average accuracy was 59.4%. The recall of the cross-modal retrieval model reached a maximum of 95.63%. Multi-task analysis showed that when using Genre as an auxiliary task, the Recall@50 (Number of relevant items in the top 50 results) of the image retrieval text task was 95.06%. The attribute deep fusion strategy based on prompt templates and the multi-task collaborative learning framework are the key to improving model performance. The combination of the two can effectively capture the specific semantics in the art field and achieve more accurate cross-modal alignment. The research results provide efficient and innovative solutions for solving cross-modal retrieval problems in art and design.
Keywords: Cross-modal retrieval; attribute features; collaborative learning; art design; prompt template.
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Citation: Guo, Y. (2027). Cross-Modal Retrieval in Art Design via Attribute Fusion and Multi-Task Collaborative Learning. Journal of Engineering, Project, and Production Management, 17(1), 2026-19.
