  Video understanding requires reasoning at multiple spatiotemporal resolutions
-- from short fine-grained motions to events taking place over longer
durations. Although transformer architectures have recently advanced the
state-of-the-art, they have not explicitly modelled different spatiotemporal
resolutions. To this end, we present Multiview Transformers for Video
Recognition (MTV). Our model consists of separate encoders to represent
different views of the input video with lateral connections to fuse information
across views. We present thorough ablation studies of our model and show that
MTV consistently performs better than single-view counterparts in terms of
accuracy and computational cost across a range of model sizes. Furthermore, we
achieve state-of-the-art results on six standard datasets, and improve even
further with large-scale pretraining. Code and checkpoints are available at:
https://github.com/google-research/scenic/tree/main/scenic/projects/mtv.
