Scholarlink

<p>Hashing-based unsupervised cross-modal retrieval has gained significant attention in the big data management community due to its low storage overhead and rapid retrieval speed. However, current methods often lack effective alignment strategies to reduce the modality gap. They also fail to explore the latent structural information of the training data for accurate relationship learning, resulting in sub-optimal cross-modal retrieval performance. To tackle these challenges, we propose a novel unsupervised cross-modal hashing method called <Emphasis Type="BoldUnderline">G</Emphasis>raph <Emphasis Type="BoldUnderline">C</Emphasis>ontrastive-and-<Emphasis Type="BoldUnderline">R</Emphasis>econstructive <Emphasis Type="BoldUnderline">H</Emphasis>ashing (<span>GCRH</span>). Specifically, <span>GCRH</span> first performs <i>global graph contrastive learning</i>, which involves both intra-modal and inter-modal pairs. This facilitates the learning of more discriminative hash codes through intra-modal discrimination and inter-modal alignment objectives. To further bridge the modality gap, <span>GCRH</span> conducts <i>local graph reconstruction</i> using GCN-based decoders to reconstruct the original features of one modality from the hash codes of another. The integration of contrastive-and-reconstructive learning with graph structural information enables <span>GCRH</span> to generate high-quality hash codes that are both well-aligned and discriminative. Extensive experiments on three benchmark datasets substantiate the superior cross-modal retrieval performance of <span>GCRH</span>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Graph Contrastive-and-Reconstructive Hashing for Unsupervised Cross-Modal Retrieval

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月8日

查看全文
  • Rukai Wei,
  • Yu Liu,
  • Heng Cui,
  • Yanzhao Xie,
  • Ke Zhou

摘要

Hashing-based unsupervised cross-modal retrieval has gained significant attention in the big data management community due to its low storage overhead and rapid retrieval speed. However, current methods often lack effective alignment strategies to reduce the modality gap. They also fail to explore the latent structural information of the training data for accurate relationship learning, resulting in sub-optimal cross-modal retrieval performance. To tackle these challenges, we propose a novel unsupervised cross-modal hashing method called Graph Contrastive-and-Reconstructive Hashing (GCRH). Specifically, GCRH first performs global graph contrastive learning, which involves both intra-modal and inter-modal pairs. This facilitates the learning of more discriminative hash codes through intra-modal discrimination and inter-modal alignment objectives. To further bridge the modality gap, GCRH conducts local graph reconstruction using GCN-based decoders to reconstruct the original features of one modality from the hash codes of another. The integration of contrastive-and-reconstructive learning with graph structural information enables GCRH to generate high-quality hash codes that are both well-aligned and discriminative. Extensive experiments on three benchmark datasets substantiate the superior cross-modal retrieval performance of GCRH.