  Graph convolutional networks (GCNs) have been very successful in modeling
non-Euclidean data structures, like sequences of body skeletons forming actions
modeled as spatio-temporal graphs. Most GCN-based action recognition methods
use deep feed-forward networks with high computational complexity to process
all skeletons in an action. This leads to a high number of floating point
operations (ranging from 16G to 100G FLOPs) to process a single sample, making
their adoption in restricted computation application scenarios infeasible. In
this paper, we propose a temporal attention module (TAM) for increasing the
efficiency in skeleton-based action recognition by selecting the most
informative skeletons of an action at the early layers of the network. We
incorporate the TAM in a light-weight GCN topology to further reduce the
overall number of computations. Experimental results on two benchmark datasets
show that the proposed method outperforms with a large margin the baseline
GCN-based method while having 2.9 times less number of computations. Moreover,
it performs on par with the state-of-the-art with up to 9.6 times less number
of computations.
