  The task of skeleton-based action recognition remains a core challenge in
human-centred scene understanding due to the multiple granularities and large
variation in human motion. Existing approaches typically employ a single neural
representation for different motion patterns, which has difficulty in capturing
fine-grained action classes given limited training data. To address the
aforementioned problems, we propose a novel multi-granular spatio-temporal
graph network for skeleton-based action classification that jointly models the
coarse- and fine-grained skeleton motion patterns. To this end, we develop a
dual-head graph network consisting of two interleaved branches, which enables
us to extract features at two spatio-temporal resolutions in an effective and
efficient manner. Moreover, our network utilises a cross-head communication
strategy to mutually enhance the representations of both heads. We conducted
extensive experiments on three large-scale datasets, namely NTU RGB+D 60, NTU
RGB+D 120, and Kinetics-Skeleton, and achieves the state-of-the-art performance
on all the benchmarks, which validates the effectiveness of our method.
