  In recent years, graph convolutional networks (GCNs) play an increasingly
critical role in skeleton-based human action recognition. However, most
GCN-based methods still have two main limitations: 1) They only consider the
motion information of the joints or process the joints and bones separately,
which are unable to fully explore the latent functional correlation between
joints and bones for action recognition. 2) Most of these works are performed
in the supervised learning way, which heavily relies on massive labeled
training data. To address these issues, we propose a semi-supervised
skeleton-based action recognition method which has been rarely exploited
before. We design a novel correlation-driven joint-bone fusion graph
convolutional network (CD-JBF-GCN) as an encoder and use a pose prediction head
as a decoder to achieve semi-supervised learning. Specifically, the CD-JBF-GC
can explore the motion transmission between the joint stream and the bone
stream, so that promoting both streams to learn more discriminative feature
representations. The pose prediction based auto-encoder in the self-supervised
training stage allows the network to learn motion representation from unlabeled
data, which is essential for action recognition. Extensive experiments on two
popular datasets, i.e. NTU-RGB+D and Kinetics-Skeleton, demonstrate that our
model achieves the state-of-the-art performance for semi-supervised
skeleton-based action recognition and is also useful for fully-supervised
methods.
