北京航空航天大学学报2026，Vol.52Issue(9)：3075-3088,14.DOI:10.13700/j.bh.1001-5965.2025.0549
细粒度语义增强的民航图文跨模态检索方法
Fine-grained semantic-enhanced cross-modal image-text retrieval method for civil aviation
摘要
Abstract
In the fields of low-altitude economy and civil aviation,security assurance tasks heavily rely on the efficient correlation of cross-modal information such as images and texts.However,while mainstream cross-modal retrieval models perform well on general datasets,they underperform in these areas,which require high levels of fine-grained semantic understanding.Based on existing civil aviation datasets,a cross-modal retrieval approach with fine-grained semantic augmentation is suggested as a solution to this problem,creating a whole pipeline that includes data processing,model development,and training.First,text descriptions are optimized and enhanced based on a large multimodal model to construct a cross-modal retrieval dataset containing rich semantic information.Second,a model is created using a popular cross-modal retrieval framework.To improve the model's ability to express fine-grained semantic features,techniques such class supervision,key semantic information masking,and a fine-grained feature extraction module are introduced.Experimental results on two datasets verify the effectiveness of the proposed method,providing a reference technical path for cross-modal retrieval in low-altitude economy scenarios.关键词
低空经济/民用航空/跨模态检索/文本描述优化/细粒度语义增强Key words
low-altitude economy/civil aviation/cross-modal retrieval/text description optimization/fine-grained semantic enhancement分类
航空航天引用本文复制引用
刘姝妍,何柳,曾江辉..细粒度语义增强的民航图文跨模态检索方法[J].北京航空航天大学学报,2026,52(9):3075-3088,14.基金项目
工业和信息化部民机专项科研项目(MJZ2-3N21) Civil Aircraft Special Research Project of the Ministry of Industry and Information Technology(MJZ2-3N21) （MJZ2-3N21）
