• In this paper they propose a new contrastive self-supervised learning method for action recognition of unlabelled data.
• The main objective is to get effective feature representations of skeleton sequences under a simple contrast learning framework.
• Inspired from many recent studies  like "Learning Representations by Maximizing Mutual Information Across Views"

Main Objectives:

INTRODUCTION:

• Compared with RGB images, body joint time-series (skeletons) are effective descriptors of actions, which are robust against the background and lighting changes.
• Although recent methods have achieved remarkable progress with the development of deep neural networks, most methods rely on strong supervision on action labels.
• The key challenges in action recognition lies in the large variety of action representation when motions are captured from different viewpoints.

Related Work:

• Unsupervised Learning in Skeleton Action Recognition:

• Recurrent-based sequence (Seq2Seq) model
• GAN encoder-decoder(LongTGAN)

• Variant of Viewpoints and Distance in Skeleton Action Recognition:

• View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition

Methodology:

Given a skeleton sequence S with T frames, under the global coordinate system, we denote the set of joints in the t-th frame as

Contrastive Learning Framework:

Experimental Protocol:

1) Datasets:

2) Model Setting:

• Larger batch sizes.
• Layer-wise Adaptive Rate Scaling (LARS) as Optimizer.

To understand the effects of individual transformation of the skeleton and the importance of transformations composition, the experiment will be repeated multiple times