Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT

• URL: http://arxiv.org/abs/2501.06786v1
• Date: Sun, 12 Jan 2025 11:48:19 GMT
• Title: Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT
• Authors: Zihao Mei, Jianhao Li, Bolin Zhang, Chong Wang, Lijun Guo, Guoqi Li, Jiangbo Qian,
• Abstract summary: We propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Based on the binary characteristics of neural networks (SNNs), we first propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Experiments on multiple datasets demonstrate that Spikinghash can achieve state-of-the-art results with low energy consumption fewer parameters.
• Score: 21.43756642033915
• License:
• Abstract: With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which can keep the distance between hash codes consistent with the distance between DVS data. As spiking neural networks (SNNs) can encode information through spikes, they demonstrate great potential in promoting energy efficiency. Based on the binary characteristics of SNNs, we first propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Spiking WaveMixer (SWM) is deployed in shallow layers, utilizing a multilevel 3D discrete wavelet transform (3D-DWT) to decouple spatiotemporal features into various low-frequency and high frequency components, and then employing efficient spectral feature fusion. SWM can effectively capture the temporal dependencies and local spatial features. Spiking Self-Attention (SSA) is deployed in deeper layers to further extract global spatiotemporal information. We also design a hash layer utilizing binary characteristic of SNNs, which integrates information over multiple time steps to generate final hash codes. Furthermore, we propose a new dynamic soft similarity loss for SNNs, which utilizes membrane potentials to construct a learnable similarity matrix as soft labels to fully capture the similarity differences between classes and compensate information loss in SNNs, thereby improving retrieval performance. Experiments on multiple datasets demonstrate that Spikinghash can achieve state-of-the-art results with low energy consumption and fewer parameters.

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