Local Graph Convolutional Networks for Cross-Modal Hashing

Cross-modal hashing aims to map the data of different modalities into a common binary space to accelerate the retrieval speed. Recently, deep cross-modal hashing methods have shown promising performance by applying deep neural networks to facilitate feature learning. However, the known supervised deep methods mainly rely on the labeled information of datasets, which is insufficient to characterize the latent structures that exist among different modalities. To mitigate this problem, in this paper, we propose to use Graph Convolutional Networks (GCNs) to exploit the local structure information of datasets for cross-modal hash learning. Specifically, a local graph is constructed according to the neighborhood relationships between samples in deep feature spaces and fed into GCNs to generate graph embeddings. Then, a within-modality loss is designed to measure the inner products between deep features and graph embeddings so that hashing networks and GCNs can be jointly optimized. By taking advantage of GCNs to assist model's training, the performance of hashing networks can be improved. Extensive experiments on benchmarks verify the effectiveness of the proposed method.