Unsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing

• URL: http://arxiv.org/abs/2606.31517v1
• Date: Tue, 30 Jun 2026 11:30:53 GMT
• Title: Unsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing
• Abstract summary: unsupervised cross-modal hashing (CMH) reduces the reliance on manual labeling by learning binary codes from unlabeled image-text pairs.<n>We propose Global-Neighborhood Alignment Hashing (GNAH), a novel approach that preserves the semantic structure of vision-language foundation models within a compact binary Hamming space.
• Score: 33.22221387810735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared to supervised cross-modal hashing (CMH), unsupervised CMH reduces the reliance on manual labeling by learning binary codes from unlabeled image-text pairs. However, existing unsupervised CMH methods often rely on large-scale image-text pairs, which are costly to collect. To address this limitation, we propose Global-Neighborhood Alignment Hashing (GNAH), a novel approach that preserves the semantic structure of vision-language foundation models within a compact binary Hamming space using only a limited number of image-text pairs. Specifically, GNAH captures global structural information from the continuous latent space and transfers it into the binary Hamming space through a Prototype-Anchored Global Alignment module. In addition, GNAH extends conventional pairwise contrastive learning by modeling stochastic neighborhood relationships via a Contrastive Stochastic Neighborhood Alignment module, thereby alleviating overfitting to sparse pairwise correlations. Extensive experiments demonstrate that GNAH consistently outperforms existing unsupervised cross-modal retrieval methods under data-constrained settings, offering a practical solution for real-world CMH applications.

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