  Modelling various spatio-temporal dependencies is the key to recognising
human actions in skeleton sequences. Most existing methods excessively relied
on the design of traversal rules or graph topologies to draw the dependencies
of the dynamic joints, which is inadequate to reflect the relationships of the
distant yet important joints. Furthermore, due to the locally adopted
operations, the important long-range temporal information is therefore not well
explored in existing works. To address this issue, in this work we propose
LSTA-Net: a novel Long short-term Spatio-Temporal Aggregation Network, which
can effectively capture the long/short-range dependencies in a spatio-temporal
manner. We devise our model into a pure factorised architecture which can
alternately perform spatial feature aggregation and temporal feature
aggregation. To improve the feature aggregation effect, a channel-wise
attention mechanism is also designed and employed. Extensive experiments were
conducted on three public benchmark datasets, and the results suggest that our
approach can capture both long-and-short range dependencies in the space and
time domain, yielding higher results than other state-of-the-art methods. Code
available at https://github.com/tailin1009/LSTA-Net.
