跨模态检索研究进展综述

Survey of Research Progress on Cross-modal Retrieval

随着互联网上多媒体数据的爆炸式增长,单一模态的检索已经无法满足用户需求,跨模态检索应运而生。跨模态检索旨在以一种模态的数据去检索另一种模态的相关数据,其核心任务是数据特征提取和不同模态间数据的相关性度量。文中梳理了跨模态检索领域近期的研究进展,从传统方法、深度学习方法、手工特征的哈希编码方法以及深度学习的哈希编码方法等角度归纳论述了跨模态检索领域的研究成果。在此基础上,对比分析了各类算法在跨模态检索常用标准数据集上的性能。最后,分析了跨模态检索研究存在的问题,并对该领域未来发展趋势以及应用进行了展望。

With the explosive growth of multimedia data on the Internet,single-modal retrieval has been unable to meet the needs of users,and cross-modal retrieval has emerged.Cross-modal retrieval aims to retrieve related data of one modality with data of another modality.Its core task is to extract data features and measure data correlation between different modality.This paper summarizes the recent research progress in the field of cross-modal retrieval,and summarizes the research results in the field of cross-modal retrieval from the perspectives of traditional methods,deep learning methods,manual feature hash coding methods and deep learning hash coding methods.On this basis,the performance of various algorithms in cross-modal retrieval of commonly used standard data sets is compared and analyzed.Finally,the problems of cross-modal retrieval research are analyzed and the future development trend of the field is prospected.