Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition

Zhen Huang

Abstract:

Skeleton-based human action recognition has attracted much attention with the prevalence of accessible depth sensors. Recently,
graph convolutional networks (GCNs) have been widely used for
this task due to their powerful capability to model graph data. The
topology of the adjacency graph is a key factor for modeling the
correlations of the input skeletons. Thus, previous methods mainly
focus on the design/learning of the graph topology. But once the
topology is learned, only a single-scale feature and one transformation exist in each layer of the networks. Many insights, such
as multi-scale information and multiple sets of transformations,
that have been proven to be very effective in convolutional neural networks (CNNs), have not been investigated in GCNs. The
reason is that, due to the gap between graph-structured skeleton
data and conventional image/video data, it is very challenging to
embed these insights into GCNs. To overcome this gap, we reinvent
the split-transform-merge strategy in GCNs for skeleton sequence
processing. Specifically, we design a simple and highly modularized
graph convolutional network architecture for skeleton-based action
recognition. Our network is constructed by repeating a building
block that aggregates multi-granularity information from both the
spatial and temporal paths. Extensive experiments demonstrate that
our network outperforms state-of-the-art methods by a significant
margin with only 1/5 of the parameters and 1/10 of the FLOPs.