TEFormer: Structured Bidirectional Temporal Enhancement Modeling in Spiking Transformers

Sicheng Shen, Mingyang Lv, Bing Han +4

In recent years, Spiking Neural Networks (SNNs) have achieved remarkable progress, with Spiking Transformers emerging as a promising architecture for energy-efficient sequence mode…