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arXiv · 2505.14719

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

Wei Hua·Chenlin Zhou·Jibin Wu·Yansong Chua·Yangyang Shu
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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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Wei Hua, Chenlin Zhou, Jibin Wu, Yansong Chua, Yangyang Shu. 2025-05-19. MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion. https://arxiv.org/abs/2505.14719

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