MCCN: Multimodal Coordinated Clustering Network for Large-Scale Cross-modal Retrieval

Cross-modal retrieval is an important multimedia research area which aims to take one type of data as the query to retrieve relevant data of another type. Most of the existing methods follow the paradigm of pair-wise learning and class-level learning to generate a common embedding space, where the similarity of heterogeneous multimodal samples can be calculated. However, in contrast to large-scale cross-modal retrieval applications which often need to tackle multiple modalities, previous studies on cross-modal retrieval mainly focus on two modalities (i.e., text-image or text-video). In addition, for large-scale cross-modal retrieval with modality diversity, another important problem is that the available training data are considerably modality-imbalanced. In this paper, we focus on the challenging problem of modality-imbalanced cross-modal retrieval, and propose a Multimodal Coordinated Clustering Network (MCCN) which consists of two modules, Multimodal Coordinated Embedding (MCE) module to alleviate the imbalanced training data and Multimodal Contrastive Clustering (MCC) module to tackle the imbalanced optimization. The MCE module develops a data-driven approach to coordinate multiple modalities via multimodal semantic graph for the generation of modality-balanced training samples. The MCC module learns class prototypes as anchors to preserve the pair-wise and class-level similarities across modalities for intra-class compactness and inter-class separation, and further introduces intra-class and inter-class margins to enhance optimization flexibility. We conduct experiments on the benchmark multimodal datasets to verify the effectiveness of our proposed method.