3 citations · 7 across the 4 of their papers we have counts for
4 papers
Signal-SGN++: Topology-Enhanced Time-Frequency Spiking Graph Network for Skeleton-Based Action Recognition
Naichuan Zheng, Xiahai Lun, Weiyi Li +1
Graph Convolutional Networks (GCNs) demonstrate strong capability in modeling skeletal topology for action recognition, yet their dense floating-point computations incur high energ…
SNN-Driven Multimodal Human Action Recognition via Sparse Spatial-Temporal Data Fusion
Naichuan Zheng, Hailun Xia, Zeyu Liang +1
Multimodal human action recognition based on RGB and skeleton data fusion, while effective, is constrained by significant limitations such as high computational complexity, excessi…
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics
Naichuan Zheng, Yuchen Du, Hailun Xia +1
For multimodal skeleton-based action recognition, Graph Convolutional Networks (GCNs) are effective models. Still, their reliance on floating-point computations leads to high energ…
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition
Naichuan Zheng, Hailun Xia, Zeyu Liang +1
In recent years, multimodal Graph Convolutional Networks (GCNs) have achieved remarkable performance in skeleton-based action recognition. The reliance on high-energy-consuming con…