基于二重语义相关性图卷积网络的跨模态检索方法OA北大核心CSTPCD

With the continuous development of deep neural networks,significant progress has been made in the construction of cross-modal retrieval models.Cross-modal retrieval methods based on GCN have shown promising results in capturing semantic correlations in data,thus attracting increasing attention.However,most recent research focuses on incorporating correlations between labels and between samples into cross-modal representations,while the impact of correlations between label sets is neglected.In multi-label scenarios,the correlations between label sets can effectively describe semantic relationships between corresponding samples.Therefore,exploring the multi-label correlations and integrating it into cross-modal representations is important for improving the performance of cross-modal retrieval models.This paper proposed a dual semantic correlation graph convolutional networks(DSCGCN)cross-modal retrieval method.This method utilized GCN to explore the semantic correla-tions between labels and between multi-labels adaptively,and integrated the learned dual semantic correlations into the com-mon representations.Additionally,it designed a multi-label similarity loss to make the similarities between the common repre-sentations more close to the semantic similarities.Experimental results on the NUS-WIDE,MIRFlickr-25 K,and MS-COCO datasets demonstrate that because of multi-label correlations,DSCGCN achieves satisfactory retrieval performance.