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arXiv · 2202.02232

Bootstrapped Representation Learning for Skeleton-Based Action Recognition

Olivier Moliner·Sangxia Huang·Kalle Åström
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Abstract

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.

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BibTeXRIS

Olivier Moliner, Sangxia Huang, Kalle Åström. 2022-02-04. Bootstrapped Representation Learning for Skeleton-Based Action Recognition. https://arxiv.org/abs/2202.02232

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