Graph Attention Hashing via Contrastive Learning for Unsupervised Cross-Modal Retrieval

Hashing-based cross-modal retrieval maps multi-modal features into binary codes into a common Hamming space. Due to its small storage consumption and high efficiency, hashing has received extensive attention in recent years. However, the current researches have difficulty in constructing a well-defined joint semantic space and conduct more detailed and in-depth learning guidance. In this paper, Graph Attention Hashing via Contrastive Learning (GAHCL) is proposed to address these issues. First, we use the idea of contrastive learning to generate positive samples, and propose a novel contrastive adjacency matrix through a graph attention network. Specifically, this matrix assigns higher weights to node pairs whose source is the same sample, and assigns lower weights to node pairs that do not match each other. The key semantic features can be captured more carefully and accurately under the influence of attention weights. In addition, the contrastive loss function is constructed by taking the output features of different modalities in an instance and its generated positive sample features as a positive sample pair. Extensive experiments on two datasets show that the proposed method can significantly outperform existing competitors.