  Unsupervised skeleton based action recognition has achieved remarkable
progress recently. Existing unsupervised learning methods suffer from severe
overfitting problem, and thus small networks are used, significantly reducing
the representation capability. To address this problem, the overfitting
mechanism behind the unsupervised learning for skeleton based action
recognition is first investigated. It is observed that the skeleton is already
a relatively high-level and low-dimension feature, but not in the same manifold
as the features for action recognition. Simply applying the existing
unsupervised learning method may tend to produce features that discriminate the
different samples instead of action classes, resulting in the overfitting
problem. To solve this problem, this paper presents an Unsupervised
spatial-temporal Feature Enrichment and Fidelity Preservation framework
(U-FEFP) to generate rich distributed features that contain all the information
of the skeleton sequence. A spatial-temporal feature transformation subnetwork
is developed using spatial-temporal graph convolutional network and graph
convolutional gate recurrent unit network as the basic feature extraction
network. The unsupervised Bootstrap Your Own Latent based learning is used to
generate rich distributed features and the unsupervised pretext task based
learning is used to preserve the information of the skeleton sequence. The two
unsupervised learning ways are collaborated as U-FEFP to produce robust and
discriminative representations. Experimental results on three widely used
benchmarks, namely NTU-RGB+D-60, NTU-RGB+D-120 and PKU-MMD dataset, demonstrate
that the proposed U-FEFP achieves the best performance compared with the
state-of-the-art unsupervised learning methods. t-SNE illustrations further
validate that U-FEFP can learn more discriminative features for unsupervised
skeleton based action recognition.
