  We explore a new perspective on video understanding by casting the video
recognition problem as an image recognition task. Our approach rearranges input
video frames into super images, which allow for training an image classifier
directly to fulfill the task of action recognition, in exactly the same way as
image classification. With such a simple idea, we show that transformer-based
image classifiers alone can suffice for action recognition. In particular, our
approach demonstrates strong and promising performance against SOTA methods on
several public datasets including Kinetics400, Moments In Time,
Something-Something V2 (SSV2), Jester and Diving48. We also experiment with the
prevalent ResNet image classifiers in computer vision to further validate our
idea. The results on both Kinetics400 and SSV2 are comparable to some of the
best-performed CNN approaches based on spatio-temporal modeling. Our source
codes and models are available at https://github.com/IBM/sifar-pytorch.
