  Spatio-temporal graph signal analysis has a significant impact on a wide
range of applications, including hand/body pose action recognition. To achieve
effective analysis, spatio-temporal graph convolutional networks (ST-GCN)
leverage the powerful learning ability to achieve great empirical successes;
however, those methods need a huge amount of high-quality training data and
lack theoretical interpretation. To address this issue, the spatio-temporal
graph scattering transform (ST-GST) was proposed to put forth a theoretically
interpretable framework; however, the empirical performance of this approach is
constrainted by the fully mathematical design. To benefit from both sides, this
work proposes a novel complementary mechanism to organically combine the
spatio-temporal graph scattering transform and neural networks, resulting in
the proposed spatio-temporal graph complementary scattering networks (ST-GCSN).
The essence is to leverage the mathematically designed graph wavelets with
pruning techniques to cover major information and use trainable networks to
capture complementary information. The empirical experiments on hand pose
action recognition show that the proposed ST-GCSN outperforms both ST-GCN and
ST-GST.
