48 citations · 52 across the 7 of their papers we have counts for
6 papers · 1 filter
Spike2Former: Efficient Spiking Transformer for High-performance Image Segmentation
Zhenxin Lei, Man Yao, Jiakui Hu +4
Spiking Neural Networks (SNNs) have a low-power advantage but perform poorly in image segmentation tasks. The reason is that directly converting neural networks with complex archit…
Efficient 3D Recognition with Event-driven Spike Sparse Convolution
Xuerui Qiu, Man Yao, Jieyuan Zhang +5
Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs should be w…
Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training
Man Yao, Xuerui Qiu, Tianxiang Hu +7
The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major c…
MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network
Xiaoshan Wu, Weihua He, Man Yao +3
Event cameras are considered to have great potential for computer vision and robotics applications because of their high temporal resolution and low power consumption characteristi…
Attention Spiking Neural Networks
Man Yao, Guangshe Zhao, Hengyu Zhang +5
Benefiting from the event-driven and sparse spiking characteristics of the brain, spiking neural networks (SNNs) are becoming an energy-efficient alternative to artificial neural n…
Temporal-wise Attention Spiking Neural Networks for Event Streams Classification
Man Yao, Huanhuan Gao, Guangshe Zhao +4
How to effectively and efficiently deal with spatio-temporal event streams, where the events are generally sparse and non-uniform and have the microsecond temporal resolution, is o…