S³GCN: SPORT SCORING SIAMESE GRAPH CONVOLUTION NETWORK

Temporal sequences of human body key points provide detailed motion information, serving as a crucial foundation for human action analysis. Existing public methods and datasets predominantly focus on action category estimation, lacking a comprehensive evaluation of sport scoring. In this work, we propose a novel model of sport scoring called Sport Scoring Siamese Graph Convolution Network(S³GCN)¹, which surpasses the constraints inherent in prior methods by implicitly capturing nuanced differences between teacher pose and student pose. In a Few-shot dataset, Taichi, it achieves a benchmark level of performance through spacial and temporal augmentation with comprehensive ablation experiments. Furthermore, our approach outperforms the original model on classification, including NTU-RGB-D and Taichi classification datasets.