Research on Optimization of Cross-Modal Retrieval Based on Ranking Loss

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，传统的跨模态检索方法主要依赖模态对齐与相似性度量，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，通过引入排序损失来优化跨模态检索过程，使得与查询相关性高的项目在结果中排名靠前，从而实现跨模态检索。实验结果表明，引入排序损失可显著提升跨模态检索性能，尤其在文本与图像匹配中表现出色，为后续研究提供了新的方法视角和坚实的技术基础。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.