多模态检索研究综述OA北大核心CSTPCD

With the increasing of multimodal data,multimodal retrieval technology has received a lot of attention.With the introduction of computer and big data technology in automobile,medical and other industries,a large amount of indus-try data itself are presented in a multi-modal form.With the rapid development of the industry,people's demand for infor-mation is constantly increasing,and single modal data retrieval can no longer meet people's demand for information.In order to solve these problems and meet the needs of data retrieval from one mode and other modes,this paper studies multi-modal retrieval methods through literature review,analyzes different research methods such as common subspace,deep learning and multi-modal Hash algorithm,and sorts out the multi-modal retrieval techniques proposed by researchers in recent years to solve these problems.Finally,the multimodal retrieval methods proposed in recent years are evaluated and compared according to the accuracy,efficiency and characteristics of the retrieval.This paper analyzes the challenges encountered in multimodal retrieval and looks forward to the future application prospects of multimodal retrieval.