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Found 3 papers, 1 papers with code
Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition
However, most of these methods tend to construct complex topology learning mechanisms while neglecting the inherent symmetry of the human body.
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics
Spiking Neural Networks (SNNs) have gained attention in recent years for their low energy consumption, but existing methods combining GCNs and SNNs fail to fully utilize the temporal characteristics of skeletal sequences, leading to increased storage and computational costs.
MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition
We propose an innovative Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation (MK-SGN) to address this issue.
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