An Animation-based Augmentation Approach for Action Recognition from
  Discontinuous Video
Xingyu Song Zhan Li Shi Chen Xin-Qiang Cai Kazuyuki Demachi
The study of action recognition has attracted considerable attention recently
due to its broad applications in multiple areas. However, with the issue of
discontinuous training video, which not only decreases the performance of
action recognition model, but complicates the data augmentation process as
well, still remains under-exploration. In this study, we introduce the 4A
(Action Animation-based Augmentation Approach), an innovative pipeline for data
augmentation to address the problem. The main contributions remain in our work
includes: (1) we investigate the problem of severe decrease on performance of
action recognition task training by discontinuous video, and the limitation of
existing augmentation methods on solving this problem. (2) we propose a novel
augmentation pipeline, 4A, to address the problem of discontinuous video for
training, while achieving a smoother and natural-looking action representation
than the latest data augmentation methodology. (3) We achieve the same
performance with only 10% of the original data for training as with all of the
original data from the real-world dataset, and a better performance on
In-the-wild videos, by employing our data augmentation techniques.