3 papers
cs.CR2026
Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks
Yi Yu, Qixin Zhang, Shuhan Ye +6
Spiking neural networks (SNNs) compute with discrete spikes and exploit temporal structure, yet most adversarial attacks change intensities or event counts instead of timing. We st…
cs.CV2026
Fire on Motion: Optimizing Video Pass-bands for Efficient Spiking Action Recognition
Shuhan Ye, Yuanbin Qian, Yi Yu +5
Spiking neural networks (SNNs) have gained traction in vision due to their energy efficiency, bio-plausibility, and inherent temporal processing. Yet, despite this temporal capacit…
cs.CV2025
Learning from Dense Events: Towards Fast Spiking Neural Networks Training via Event Dataset Distillation
Shuhan Ye, Yi Yu, Qixin Zhang +4
Event cameras sense brightness changes and output binary asynchronous event streams, attracting increasing attention. Their bio-inspired dynamics align well with spiking neural net…