  Graph convolutional networks (GCNs) achieved promising performance in
skeleton-based human action recognition by modeling a sequence of skeletons as
a spatio-temporal graph. Most of the recently proposed GCN-based methods
improve the performance by learning the graph structure at each layer of the
network using a spatial attention applied on a predefined graph Adjacency
matrix that is optimized jointly with model's parameters in an end-to-end
manner. In this paper, we analyze the spatial attention used in spatio-temporal
GCN layers and propose a symmetric spatial attention for better reflecting the
symmetric property of the relative positions of the human body joints when
executing actions. We also highlight the connection of spatio-temporal GCN
layers employing additive spatial attention to bilinear layers, and we propose
the spatio-temporal bilinear network (ST-BLN) which does not require the use of
predefined Adjacency matrices and allows for more flexible design of the model.
Experimental results show that the three models lead to effectively the same
performance. Moreover, by exploiting the flexibility provided by the proposed
ST-BLN, one can increase the efficiency of the model.
