  In this work, we propose a new Dual Min-Max Games (DMMG) based
self-supervised skeleton action recognition method by augmenting unlabeled data
in a contrastive learning framework. Our DMMG consists of a viewpoint variation
min-max game and an edge perturbation min-max game. These two min-max games
adopt an adversarial paradigm to perform data augmentation on the skeleton
sequences and graph-structured body joints, respectively. Our viewpoint
variation min-max game focuses on constructing various hard contrastive pairs
by generating skeleton sequences from various viewpoints. These hard
contrastive pairs help our model learn representative action features, thus
facilitating model transfer to downstream tasks. Moreover, our edge
perturbation min-max game specializes in building diverse hard contrastive
samples through perturbing connectivity strength among graph-based body joints.
The connectivity-strength varying contrastive pairs enable the model to capture
minimal sufficient information of different actions, such as representative
gestures for an action while preventing the model from overfitting. By fully
exploiting the proposed DMMG, we can generate sufficient challenging
contrastive pairs and thus achieve discriminative action feature
representations from unlabeled skeleton data in a self-supervised manner.
Extensive experiments demonstrate that our method achieves superior results
under various evaluation protocols on widely-used NTU-RGB+D and NTU120-RGB+D
datasets.
