BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning
Spiking Vision Transformers (S-ViTs) offer a promising framework for energy-efficient visual learning. However, existing designs remain limited by two fundamental issues: the restricted information capacity of binary spike coding and the dense token interactions introduced by global self-attention. To address these challenges, this work proposes BSViT, a burst spiking-driven Vision Transformer featuring a Dual-Channel Burst Spiking Self-Attention (DBSSA) mechanism. DBSSA encodes queries with binary spikes and keys with burst spikes to enhance representational capacity. The value pathway adopts dual excitatory and inhibitory binary channels, enabling signed modulation and richer spike inte...
| Authors | Hongxiang Peng, Dewei Bai, Hong Qu |
|---|---|
| Primary category | cs.CV |
| Published | 25 April 2026 |
| Last updated at source | 11 June 2026 |
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