1 citations · 1 across the 6 of their papers we have counts for
7 papers
Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks
Yongqi Ding, Kunshan Yang, Linze Li +3
Although the temporal spike dynamics of spiking neural networks (SNNs) enable low-power temporal pattern capture capabilities, they also incur inherent inconsistencies that severel…
Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers
Yongqi Ding, Lin Zuo, Mengmeng Jing +3
Brain-inspired spiking neural networks (SNNs) promise to be a low-power alternative to computationally intensive artificial neural networks (ANNs), although performance gaps persis…
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition
Kunshan Yang, Wenwei Luo, Yuguo Hu +3
Flexible objects recognition remains a significant challenge due to its inherently diverse shapes and sizes, translucent attributes, and subtle inter-class differences. Graph-based…
Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
Lin Zuo, Yongqi Ding, Mengmeng Jing +3
This paper explores the application of spiking neural networks (SNNs), known for their low-power binary spikes, to bearing fault diagnosis, bridging the gap between high-performanc…
Temporal Reversal Regularization for Spiking Neural Networks: Hybrid Spatio-Temporal Invariance for Generalization
Lin Zuo, Yongqi Ding, Wenwei Luo +2
Spiking neural networks (SNNs) have received widespread attention as an ultra-low power computing paradigm. Recent studies have shown that SNNs suffer from severe overfitting, whic…
Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks
Lin Zuo, Yongqi Ding, Mengmeng Jing +2
Spiking neural networks (SNNs) have attracted considerable attention for their event-driven, low-power characteristics and high biological interpretability. Inspired by knowledge d…
Flexible ViG: Learning the Self-Saliency for Flexible Object Recognition
Lin Zuo, Kunshan Yang, Xianlong Tian +3
Existing computer vision methods mainly focus on the recognition of rigid objects, whereas the recognition of flexible objects remains unexplored. Recognizing flexible objects pose…