Uncertainty-Aware Token Importance Estimation in Spiking Transformers
Spiking transformers have shown strong potential for neuromorphic vision, yet their token processing across multiple spiking steps still introduces substantial redundancy and inference cost. Existing token reduction methods mainly rely on response based cues, such as activation magnitude, firing statistics, or feature similarity. Although effective, these criteria do not explicitly characterize token importance from the perspective of temporally evolving class evidence. In spiking transformers, token representations are progressively formed across multiple spiking steps rather than determined at a single instant, suggesting that token importance should be evaluated not only by instantaneo...
| Authors | Wenxuan Liu, Zecheng Hao, Tong Bu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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