MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion

• URL: http://arxiv.org/abs/2505.14719v3
• Date: Wed, 18 Jun 2025 03:58:23 GMT
• Title: MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion
• Authors: Wei Hua, Chenlin Zhou, Jibin Wu, Yansong Chua, Yangyang Shu,
• Abstract summary: Spiking Neural Networks (SNNs) with Vision Transformer architectures has garnered significant attention due to their potential for energy-efficient and high-performance computing.<n>A substantial performance gap still exists between SNN-based and ANN-based transformer architectures.<n>We present a novel spike-driven Transformer architecture using multi-scale spiking attention (MSSA) to enhance the capabilities of spiking attention blocks.
• Score: 10.715931690834127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The combination of Spiking Neural Networks (SNNs) with Vision Transformer architectures has garnered significant attention due to their potential for energy-efficient and high-performance computing paradigms. However, a substantial performance gap still exists between SNN-based and ANN-based transformer architectures. While existing methods propose spiking self-attention mechanisms that are successfully combined with SNNs, the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting features from different image scales. In this paper, we address this issue and propose MSVIT. This novel spike-driven Transformer architecture firstly uses multi-scale spiking attention (MSSA) to enhance the capabilities of spiking attention blocks. We validate our approach across various main datasets. The experimental results show that MSVIT outperforms existing SNN-based models, positioning itself as a state-of-the-art solution among SNN-transformer architectures. The codes are available at https://github.com/Nanhu-AI-Lab/MSViT.

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