Abstract: With the rapid development of multimedia technology,cross-media retrieval has gradually replaced traditional single-media retrieval as the mainstream information retrieval method.Existing cross-media retrieval methods are highly complex,and cannot fully mine the detailed characteristics of the data,which will cause deviations in the mapping process,and it is difficult to learn accurate data associations.To solve the above problems,this paper proposes a cross-media retrieval method based onresidualattention network(CR-RAN).First of all,in order to better extract the key features of different media data and simplify the cross-media retrieval model,this paper proposes a residual neural network incorporating the attention mechanism.Then this paper proposes a cross-media retrieval joint loss function,which enhances the semantic discrimination ability of the network and improves the accuracy of network retrieval by constraining the mapping process of the network.Experimental results show that,compared with some existing methods,the cross-media retrieval method based on residual attention network proposed in this paper can better learn the association between different media data and effectively improve the accuracy of cross-media retrieval.

Key words: Attention mechanism, Cross media retrieval, Joint loss function, Residual neural network