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

[2501.06786] Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2501.06786 (cs) [Submitted on 12 Jan 2025 ( v1 ), last revised 11 Apr 2026 (this version, v2)] 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 View a PDF of the paper titled Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT, by Zihao Mei and 6 other authors View PDF HTML (experimental) 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. Comments: TCYB under review. This work has been submitted to the lEEE for possible publication Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2501.06786 [cs.CV] (or arXiv:2501.06786v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2501.06786 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zihao Mei [ view email ] [v1] Sun, 12 Jan 2025 11:48:19 UTC (448 KB) [v2] Sat, 11 Apr 2026 01:25:59 UTC (309 KB) Full-text links: Access Paper: View a PDF of the paper titled Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT, by Zihao Mei and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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