Skeleton-Contrastive 3D Action Representation Learning

• URL: http://arxiv.org/abs/2108.03656v1
• Date: Sun, 8 Aug 2021 14:44:59 GMT
• Title: Skeleton-Contrastive 3D Action Representation Learning
• Authors: Fida Mohammad Thoker, Hazel Doughty, Cees G.M. Snoek
• Abstract summary: This paper strives for self-supervised learning of a feature space suitable for skeleton-based action recognition. Our approach achieves state-of-the-art performance for self-supervised learning from skeleton data on the challenging PKU and NTU datasets.
• Score: 35.06361753065124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper strives for self-supervised learning of a feature space suitable for skeleton-based action recognition. Our proposal is built upon learning invariances to input skeleton representations and various skeleton augmentations via a noise contrastive estimation. In particular, we propose inter-skeleton contrastive learning, which learns from multiple different input skeleton representations in a cross-contrastive manner. In addition, we contribute several skeleton-specific spatial and temporal augmentations which further encourage the model to learn the spatio-temporal dynamics of skeleton data. By learning similarities between different skeleton representations as well as augmented views of the same sequence, the network is encouraged to learn higher-level semantics of the skeleton data than when only using the augmented views. Our approach achieves state-of-the-art performance for self-supervised learning from skeleton data on the challenging PKU and NTU datasets with multiple downstream tasks, including action recognition, action retrieval and semi-supervised learning. Code is available at https://github.com/fmthoker/skeleton-contrast.

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