Abstract
Deep network based cross-modal retrieval has recently made significant progress. However, bridging modality gap to further enhance the retrieval
accuracy still remains a crucial bottleneck. In this
paper, we propose a Graph Convolutional Hashing
(GCH) approach, which learns modality-unified
binary codes via an affinity graph. An end-to-end
deep architecture is constructed with three main
components: a semantic encoder module, two feature encoding networks, and a graph convolutional
network (GCN). We design a semantic encoder as a
teacher module to guide the feature encoding process, a.k.a. student module, for semantic information exploiting. Furthermore, GCN is utilized to explore the inherent similarity structure among data
points, which will help to generate discriminative
hash codes. Extensive experiments on three benchmark datasets demonstrate that the proposed GCH
outperforms the state-of-the-art methods