%0 Journal Article
%T 基于排序损失的跨模态检索优化研究<br>Research on Optimization of Cross-Modal Retrieval Based on Ranking Loss
%A 蒋彩兰
%J Modeling and Simulation
%P 116-121
%@ 2324-870X
%D 2025
%I Hans Publishing
%R 10.12677/mos.2025.141012
%X 跨模态检索通过一种模态(如文本或图像)来检索另一模态的数据&#65292;传统的跨模态检索方法主要依赖模态对齐与相似性度量&#65292;以实现多模态间的特征匹配。本文创新性地提出了一种基于排序的跨模态检索方法&#65292;通过引入排序损失来优化跨模态检索过程&#65292;使得与查询相关性高的项目在结果中排名靠前&#65292;从而实现跨模态检索。实验结果表明&#65292;引入排序损失可显著提升跨模态检索性能&#65292;尤其在文本与图像匹配中表现出色&#65292;为后续研究提供了新的方法视角和坚实的技术基础。<br>Cross-modal retrieval aims to retrieve data in one modality (such as text or images) based on another modality. Traditional cross-modal retrieval methods primarily rely on modality alignment and similarity measures to achieve feature matching across multiple modalities. This paper presents an innovative sorting-based cross-modal retrieval method that optimizes the cross-modal retrieval process by introducing ranking loss, allowing items with higher relevance to the query to be prioritized in the results, thereby enhancing cross-modal retrieval effectiveness. Experimental results demonstrate that the introduction of ranking loss significantly enhances the performance of cross-modal retrieval, particularly excelling in text-image matching tasks. This work provides a new methodological perspective and a solid technical foundation for future research in the field.
%K 跨模态检索&#65292
%K 排序损失&#65292
%K 相似性度量<br>Cross-Modal Retrieval
%K Ranking Loss
%K Similarity Measure
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=104628