In this work, we study self-supervised representation learning for 3D
skeleton-based action recognition. We extend Bootstrap Your Own Latent (BYOL)
for representation learning on skeleton sequence data and propose a new data
augmentation strategy including two asymmetric transformation pipelines. We
also introduce a multi-viewpoint sampling method that leverages multiple
viewing angles of the same action captured by different cameras. In the
semi-supervised setting, we show that the performance can be further improved
by knowledge distillation from wider networks, leveraging once more the
unlabeled samples. We conduct extensive experiments on the NTU-60 and NTU-120
datasets to demonstrate the performance of our proposed method. Our method
consistently outperforms the current state of the art on both linear evaluation
and semi-supervised benchmarks.

本文研究了自监督表示学习在三维骨架动作识别中的应用，提出了一种包括两个不对称变换管道的数据增强策略和一种多视角采样方法，利用无标签样本从更宽的网络进行知识蒸馏，实验表明该方案在 NTU-60 和 NTU-120 数据集上表现优于当前的半监督方法和线性评估基准。