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%.

Exploiting Offset-guided Network for Pose Estimation and Tracking

Human pose estimation has witnessed a significant advance thanks to the development of deep learning. Recent human pose estimation approaches tend to directly predict the location heatmaps, which causes quantization errors and inevitably deteriorates the performance within the reduced network output. Aim at solving it, we revisit the heatmap-offset aggregation method and propose the Offset-guided Network (OGN) with an intuitive but effective fusion strategy for both two-stages pose estimation and Mask R-CNN. For two-stages pose estimation, a greedy box generation strategy is also proposed to keep more necessary candidates while performing person detection. For mask R-CNN, ratio-consistent is adopted to improve the generalization ability of the network. State-of-the-art results on COCO and PoseTrack dataset verify the effectiveness of our offset-guided pose estimation and tracking.