Unsupervised Contrastive Graph Kolmogorov–Arnold Networks Enhanced Cross-Modal Retrieval Hashing

Hongyu Lin (), Shaofeng Shen, Yuchen Zhang and Renwei Xia
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Hongyu Lin: Dundee International Institute of Central South University, Central South University, Changsha 410083, China
Shaofeng Shen: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Yuchen Zhang: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Renwei Xia: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Mathematics, 2025, vol. 13, issue 11, 1-21

Abstract: To address modality heterogeneity and accelerate large-scale retrieval, cross-modal hashing strategies generate compact binary codes that enhance computational efficiency. Existing approaches often struggle with suboptimal feature learning due to fixed activation functions and limited cross-modal interaction. We propose Unsupervised Contrastive Graph Kolmogorov–Arnold Networks (GraphKAN) Enhanced Cross-modal Retrieval Hashing (UCGKANH), integrating GraphKAN with contrastive learning and hypergraph-based enhancement. GraphKAN enables more flexible cross-modal representation through enhanced nonlinear expression of features. We introduce contrastive learning that captures modality-invariant structures through sample pairs. To preserve high-order semantic relations, we construct a hypergraph-based information propagation mechanism, refining hash codes by enforcing global consistency. The efficacy of our UCGKANH approach is validated by thorough tests on the MIR-FLICKR, NUS-WIDE, and MS COCO datasets, which show significant gains in retrieval accuracy coupled with strong computational efficiency.

Keywords: cross-modal retrieval; hashing; Graph Kolmogorov–Arnold Networks; contrastive learning; hypergraph neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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