【论文笔记】CVPR2020 Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition

使用GCN进行skeleton-based action recognition

Contribution

提出了两个设计：

• a disentangled multi-scale aggregation scheme
• a unified spatial-temporal graph convolutional module (G3D)

分别解决了两个问题：

• unbiased weight problem: edge weights will be biased towards closer nodes against further nodes，对于距离较远的两个节点，他们之间的feature share的效果比较轻微，由于距离太远，weight很难传过去。学习long-range relationship比较困难。例如：scale = 7，真正到距离为7的节点的几率是很小的 （这里没有完全理解）。（原始的multi-scale GCN见paper Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition ）
• factorised spatial-temporal relationship learning: A typical approach is to extract spatial relationships at each time step and then model temporal dynamics. 这样，在spacetime的三维空间里不存在直接的信息流，只能是先space，再time这样间接的提取关系。