Deep cross-modal hashing retrieval has recently made significant progress. However, existing methods generally learn hash functions with pairwise or triplet supervisions, which involves learning the relevant information by splicing partial similarity between data pairs; notably, this approach only captures the data similarity locally and incompletely, resulting in sub-optimal retrieval performance. In this paper, we propose a novel Multi-Relational Deep Hashing (MRDH) approach, which can fully bridge the modality gap by comprehensively modeling the similarity relationship between data in different modalities. In more detail, to investigate the inter-modal relationships, we constrain the consistency of cross-modal pairwise similarities to maintain the semantic similarity across modalities. Moreover, to further capture complete similarity information, we design a new similarity metric, which we term cross-modal global similarity, by encouraging hash codes of similar data pairs from different modalities to approach a common center and hash codes for dissimilar pairs to converge to different centers. Adopting this approach enables our model to generate more discriminative hash codes. Extensive experiments on three benchmark datasets demonstrate the superiority of our method on cross-modal hashing retrieval.

中文翻译：

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用于跨模式搜索的多关系深度哈希

深度跨模态哈希检索最近取得了重大进展。然而，现有的方法通常通过成对或三元组监督来学习哈希函数，这涉及通过拼接数据对之间的部分相似性来学习相关信息；值得注意的是，这种方法只能局部且不完整地捕获数据相似性，从而导致检索性能不佳。在本文中，我们提出了一种新颖的多关系深度哈希（MRDH）方法，该方法可以通过对不同模态数据之间的相似关系进行综合建模来充分弥合模态差距。更详细地说，为了研究模态间关系，我们限制跨模态成对相似性的一致性，以维持跨模态的语义相似性。此外，为了进一步捕获完整的相似性信息，我们设计了一种新的相似性度量，我们将其称为跨模态全局相似性，通过鼓励来自不同模态的相似数据对的哈希码接近公共中心，并鼓励不同数据对的哈希码收敛到不同的中心。采用这种方法使我们的模型能够生成更具辨别力的哈希码。对三个基准数据集的广泛实验证明了我们的方法在跨模式哈希检索方面的优越性。