  This paper presents VTN, a transformer-based framework for video recognition.
Inspired by recent developments in vision transformers, we ditch the standard
approach in video action recognition that relies on 3D ConvNets and introduce a
method that classifies actions by attending to the entire video sequence
information. Our approach is generic and builds on top of any given 2D spatial
network. In terms of wall runtime, it trains $16.1\times$ faster and runs
$5.1\times$ faster during inference while maintaining competitive accuracy
compared to other state-of-the-art methods. It enables whole video analysis,
via a single end-to-end pass, while requiring $1.5\times$ fewer GFLOPs. We
report competitive results on Kinetics-400 and present an ablation study of VTN
properties and the trade-off between accuracy and inference speed. We hope our
approach will serve as a new baseline and start a fresh line of research in the
video recognition domain. Code and models will be available soon.
