BRAINHash: Brain-Inspired Region-Aligned Interaction Network for Unsupervised Cross-Modal Hashing

Unsupervised cross-modal hashing (UCMH) has attracted considerable attention owing to its minimal reliance on manual annotations and low retrieval latency. However, existing UCMH methods based on contrastive learning frameworks combined with multilayer perceptrons (MLPs) often suffer from two key limitations: the inherent challenge in constructing reliable positive-negative sample pairs under unsupervised settings, and the restricted representational capacity of static network architectures. Inspired by cognitive mechanisms observed in the human brain, where specialized regions support distinct roles in knowledge acquisition and error-driven learning, we propose a Brain-inspired Region-Aligned Interaction Network for unsupervised cross-modal Hashing (BRAINHash), which is a novel brain-inspired memory-based temporal modeling strategy. BRAINHash emulates several functional components of the brain through modular design choices: 1) diverse encoders approximate feature encoding akin to occipital lobe processing, 2) an error-aware optimization strategy models initial cross-modal association construction analogous to prefrontal cortex activity, 3) a teacher network serves as hippocampal-like memory storage capturing learned representations as persistent memory points, 4) we incorporate soft-target contrastive objectives alongside spiking neural network-based temporal modeling to simulate higher-order reasoning typically attributed to prefrontal decision-making circuits. To the best of our knowledge, BRAINHash represents the first integration of biologically inspired architectural principles with temporal dynamics into an unsupervised cross-modal hashing paradigm. Extensive evaluations conducted on five widely-used datasets demonstrate that our method outperforms fifteen state-of-the-art approaches. The implementation code is publicly available at https://github.com/YSU-ISU-Lab/BRAIN.