论文标题
ObjectMix：通过在视频中复制对象进行数据增强以供行动识别
ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition
论文作者
论文摘要
在本文中，我们提出了一种使用实例分割的数据增强方法，以识别行动识别方法。尽管已经提出了许多数据增强方法用于图像识别，但其中很少有用于行动识别的量身定制的。我们提出的方法使用实例分割从两个视频中提取每个对象区域，并将它们组合起来以创建新视频。在两个动作识别数据集UCF101和HMDB51上进行了实验，证明了该方法的有效性，并显示了其优于先前工作的Videomix的优势。
In this paper, we propose a data augmentation method for action recognition using instance segmentation. Although many data augmentation methods have been proposed for image recognition, few of them are tailored for action recognition. Our proposed method, ObjectMix, extracts each object region from two videos using instance segmentation and combines them to create new videos. Experiments on two action recognition datasets, UCF101 and HMDB51, demonstrate the effectiveness of the proposed method and show its superiority over VideoMix, a prior work.
