7 citations · 12 across the 5 of their papers we have counts for
6 papers
Neuromorphic Object Detection: An In-Depth Study and Future Directions
Jianing Li, Dianze Li, Arren Glover +5
Conventional frame-based cameras face significant challenges in detecting objects under high-speed motion blur or in low-light environments. Neuromorphic cameras provide asynchrono…
Temporal-adaptive Weight Quantization for Spiking Neural Networks
Han Zhang, Qingyan Meng, Jiaqi Wang +3
Weight quantization in spiking neural networks (SNNs) could further reduce energy consumption. However, quantizing weights without sacrificing accuracy remains challenging. In this…
HDI-Former: Hybrid Dynamic Interaction ANN-SNN Transformer for Object Detection Using Frames and Events
Dianze Li, Jianing Li, Xu Liu +3
Combining the complementary benefits of frames and events has been widely used for object detection in challenging scenarios. However, most object detection methods use two indepen…
Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods
Chenlin Zhou, Han Zhang, Liutao Yu +7
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-te…
QKFormer: Hierarchical Spiking Transformer using Q-K Attention
Chenlin Zhou, Han Zhang, Zhaokun Zhou +7
Spiking Transformers, which integrate Spiking Neural Networks (SNNs) with Transformer architectures, have attracted significant attention due to their potential for energy efficien…
Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation
Chenlin Zhou, Han Zhang, Zhaokun Zhou +5
Deep spiking neural networks (SNNs) have drawn much attention in recent years because of their low power consumption, biological rationality and event-driven property. However, sta…