Skeleton-based action recognition is widely used in varied areas, e.g.,
surveillance and human-machine interaction. Existing models are mainly learned
in a supervised manner, thus heavily depending on large-scale labeled data
which could be infeasible when labels are prohibitively expensive. In this
paper, we propose a novel Contrast-Reconstruction Representation Learning
network (CRRL) that simultaneously captures postures and motion dynamics for
unsupervised skeleton-based action recognition. It mainly consists of three
parts: Sequence Reconstructor, Contrastive Motion Learner, and Information
Fuser. The Sequence Reconstructor learns representation from skeleton
coordinate sequence via reconstruction, thus the learned representation tends
to focus on trivial postural coordinates and be hesitant in motion learning. To
enhance the learning of motions, the Contrastive Motion Learner performs
contrastive learning between the representations learned from coordinate
sequence and additional velocity sequence, respectively. Finally, in the
Information Fuser, we explore varied strategies to combine the Sequence
Reconstructor and Contrastive Motion Learner, and propose to capture postures
and motions simultaneously via a knowledge-distillation based fusion strategy
that transfers the motion learning from the Contrastive Motion Learner to the
Sequence Reconstructor. Experimental results on several benchmarks, i.e., NTU
RGB+D 60, NTU RGB+D 120, CMU mocap, and NW-UCLA, demonstrate the promise of the
proposed CRRL method by far outperforming state-of-the-art approaches.

该论文提出了一种名为 CRRL 的新型对比重建表示学习网络，它通过无监督学习方式，同时捕捉姿态和运动动态，从而提高无标签数据的骨骼动作识别效果。在实验上，该方法在多个基准数据集上远远优于现有技术水平。