  Action recognition is a well-established area of research in computer vision.
In this paper, we propose S3Aug, a video data augmenatation for action
recognition. Unlike conventional video data augmentation methods that involve
cutting and pasting regions from two videos, the proposed method generates new
videos from a single training video through segmentation and label-to-image
transformation. Furthermore, the proposed method modifies certain categories of
label images by sampling to generate a variety of videos, and shifts
intermediate features to enhance the temporal coherency between frames of the
generate videos. Experimental results on the UCF101, HMDB51, and Mimetics
datasets demonstrate the effectiveness of the proposed method, paricularlly for
out-of-context videos of the Mimetics dataset.
