Modality Consistent Generative Adversarial Network for Cross-Modal Retrieval

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Cross-modal retrieval, which aims to perform the retrieval task across different modalities of data, is a hot topic. Since different modalities of data have inconsistent distributions, how to reduce the gap of different modalities is the core of cross-modal retrieval issue. Recently, Generative Adversarial Networks has been used in cross-modal retrieval due to its strong ability to model data distribution. We propose a novel approach named Modality Consistent Generative Adversarial Network for cross-modal retrieval (MCGAN). The network integrates a generator to generate synthetic image features from text features, a discriminator to classify the modality of features, and followed by a modality consistent embedding network that projects the generated image features and real image features into a common space for learning the discriminative representations. Experiments on two datasets prove the performance of MCGAN on cross-modal retrieval, compared with state-of-the-art related works.