Cross-modal hashing retrieval encodes heterogeneous data into compact binary codes for efficient Hamming-space search. Existing methods usually learn cross-modal semantics in continuous feature spaces and generate binary codes through a final sign operation, which weakly couples training optimization with discrete hash retrieval. We propose SpikeHash, a unified spiking framework that formulates cross-modal hashing as spike-state evolution, directional spike interaction, and competitive spike readout. Specifically, SpikeHash converts image and text features into multi-timestep spike sequences. In a shared Hamming space, the two spike sequences jointly drive the temporal evolution of a shared hash state. Cross-modal interaction is further performed through directional spike modulation, enabling each modality to influence the firing dynamics of the other. Crucially, SpikeHash replaces the conventional continuous hash head with a positive-negative spiking hash readout, where each hash bit is produced by temporal competition between paired spike channels. Experimental results show that SpikeHash achieves competitive retrieval accuracy on three benchmark datasets while reducing the parameter size, operation count, and estimated energy of the hash learning stage, suggesting a compact spiking alternative to conventional continuous hash mapping. The project page is available at https://shuqiao-111.github.io/.

翻译：跨模态哈希检索将异构数据编码为紧凑二值码以实现高效的汉明空间搜索。现有方法通常在连续特征空间中学习跨模态语义，并通过最终的符号运算生成二值码，这种弱耦合的训练优化方式与离散哈希检索存在割裂。本文提出SpikeHash——一种统一的脉冲框架，将跨模态哈希形式化为脉冲状态演化、方向性脉冲交互与竞争性脉冲读出三类过程。具体而言，SpikeHash将图像与文本特征转换为多时间步的脉冲序列，在共享的汉明空间中，两段脉冲序列共同驱动共享哈希状态的时间演化。进一步通过方向性脉冲调制实现跨模态交互，使每种模态能影响另一模态的脉冲发放动力学。关键创新在于，SpikeHash以正负脉冲哈希读出机制替代传统连续哈希头，其中每个哈希位由配对脉冲通道间的时间竞争产生。实验结果表明，SpikeHash在三个基准数据集上取得了具有竞争力的检索精度，同时减少了哈希学习阶段的参数量、运算次数与预估能耗，为传统连续哈希映射提供了一种紧凑的脉冲替代方案。项目页面访问地址：https://shuqiao-111.github.io/。