Deep hashing has gained widespread attention in cross-modal retrieval due to its low cost and efficient retrieval advantages. However, existing cross-modal hashing methods either focus solely on exploring relationships between data points—which inevitably leads to intra-class dispersion—or emphasize the relationships between data points and categories while neglecting the preservation of inter-class structural relationships, resulting in suboptimal hash codes. To address this issue, this paper proposes a Deep Class-Guided Hashing (DCGH) method, which approaches model learning from the category level. It ensures that data of the same category are clustered around the same class center while maintaining the structural relationships between class centers. Extensive comparative experiments conducted on three benchmark datasets demonstrate that the DCGH method achieves comparable or even superior performance compared to existing cross-modal retrieval methods.

Due to the difficulty in obtaining label information in practical applications, semi-supervised cross-modal retrieval has emerged. However, the existing semi-supervised cross-modal hashing retrieval methods mainly focus on exploring the structural relationships between data and generating high-quality discrete pseudo-labels while neglecting the relationships between data and categories, as well as the structural relationships between data and categories inherent in continuous pseudo-labels. Based on this, Proxy-based Semi-Supervised Cross-Modal Hashing (PSSCH) is proposed. Specifically, we propose a category proxy network to generate category center points in both feature and hash spaces. Additionally, we design an Adaptive Dual-Label Loss function, which applies different learning strategies to discrete ground truth labels and continuous pseudo-labels and adaptively increases the training weights of unlabeled data with more epochs. Experiments on the MIRFLICKR-25K, NUS-WIDE, and MS COCO datasets show that PSSCH achieves the highest mAP improvements of 3%, 1%, and 4%, respectively, demonstrating better results than the latest baseline methods.