MULTI-VIEW FUSION THROUGH CROSS-MODAL RETRIEVAL

Cross-modal retrieval, which takes text queries to retrieve relevant images or vice versa, has drawn much attention in recent years. This topic exhibits dual-heterogeneity: heterogeneity of different modalities and heterogeneous features obtained from multiple views. To address this issue, we propose an effective multi-view fusion method for cross-modal retrieval based on tensor modeling (CMTM) for cross-modal retrieval from the full-order feature interactions within the multimodal data. In order to facilitate integration of heterogeneous features from multiple views, we adopt the tensor structure to model the full-order interactions among the multi-view features effectively. Besides, a tensor factorization is applied to derive model parameters. Extensive experiments demonstrate the effectiveness of CMTM on cross-modal retrieval.