MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition

In recent years, skeleton-based action recognition, leveraging multimodal Graph Convolutional Networks (GCN), has achieved remarkable results. However, due to their deep structure and reliance on continuous floating-point operations, GCN-based methods are energy-intensive. To address this issue, we propose an innovative Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation (MK-SGN). By merging the energy efficiency of Spiking Neural Network (SNN) with the graph representation capability of GCN, the proposed MK-SGN reduces energy consumption while maintaining recognition accuracy. Firstly, we convert GCN into Spiking Graph Convolutional Network (SGN) and construct a foundational Base-SGN for skeleton-based action recognition, establishing a new benchmark and paving the way for future research exploration. Secondly, we further propose a Spiking Multimodal Fusion module (SMF), leveraging mutual information to process multimodal data more efficiently. Additionally, we introduce a spiking attention mechanism and design a Spatio Graph Convolution module with a Spatial Global Spiking Attention mechanism (SA-SGC), enhancing feature learning capability. Furthermore, we delve into knowledge distillation methods from multimodal GCN to SGN and propose a novel, integrated method that simultaneously focuses on both intermediate layer distillation and soft label distillation to improve the performance of SGN. On two challenging datasets for skeleton-based action recognition, MK-SGN outperforms the state-of-the-art GCN-like frameworks in reducing computational load and energy consumption. In contrast, typical GCN methods typically consume more than 35mJ per action sample, while MK-SGN reduces energy consumption by more than 98%.