Cross-modal retrieval with dual optimization

For the flexible retrieval of data in different modalities, cross-modal retrieval has gradually attracted the attention of researchers. However, there is a heterogeneity gap between the data of different modalities, which cannot be measured directly. To solve this problem, researchers project data of different modalities into a common representation space to compensate for the heterogeneity of data of different modalities. However, existing methods with pair or triple constraints ignore the rich information between samples, which leads to the degradation of retrieval performance. In order to fully mine the information of samples, this paper proposes a cross-modal retrieval method (CMRDO) with dual optimization. First, the method optimizes the common representation space from inter-modal and intra-modal, respectively. Secondly, we introduce an efficient sample construction strategy to avoid sample pairs with less information. Finally, the bi-directional retrieval strategy we introduced can effectively capture the potential structure of query modal. In the three public datasets, the proposed CMRDO can effectively improve the final cross-modal retrieval accuracy, and has strong generalization ability.