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.

研究表明，由于其在多个领域的广泛应用，动作识别研究近年来引起了相当大的关注。然而，关于不连续训练视频的问题仍然未得到充分探索，这不仅降低了动作识别模型的性能，还复杂化了数据增强过程。本研究介绍了一种创新的数据增强流程 ——4A（基于动画的动作增强方法），旨在解决该问题。我们的主要贡献包括：（1）我们调查了不连续视频对动作识别任务训练性能的严重降低问题，以及现有增强方法在解决此问题方面的局限性。（2）我们提出了一种新颖的增强流程 4A，以解决训练中不连续视频的问题，同时实现比最新的数据增强方法更平滑、更自然的动作表示。（3）通过采用我们的数据增强技术，我们仅需使用原始数据的 10％即可实现与来自现实世界数据集的全部数据训练相同的性能，并在野外视频上表现更好。