MS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition

MM '20: The 28th ACM International Conference on Multimedia Seattle WA USA October, 2020, pp. 2490-2498, 2020.

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Abstract:

In this paper, we address self-supervised representation learning from human skeletons for action recognition. Previous methods, which usually learn feature presentations from a single reconstruction task, may come across the overfitting problem, and the features are not generalizable for action recognition. Instead, we propose to integra...More

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