  This paper tackles the problem of efficient video recognition. In this area,
video transformers have recently dominated the efficiency (top-1 accuracy vs
FLOPs) spectrum. At the same time, there have been some attempts in the image
domain which challenge the necessity of the self-attention operation within the
transformer architecture, advocating the use of simpler approaches for token
mixing. However, there are no results yet for the case of video recognition,
where the self-attention operator has a significantly higher impact (compared
to the case of images) on efficiency. To address this gap, in this paper, we
make the following contributions: (a) we construct a highly efficient \&
accurate attention-free block based on the shift operator, coined Affine-Shift
block, specifically designed to approximate as closely as possible the
operations in the MHSA block of a Transformer layer. Based on our Affine-Shift
block, we construct our Affine-Shift Transformer and show that it already
outperforms all existing shift/MLP--based architectures for ImageNet
classification. (b) We extend our formulation in the video domain to construct
Video Affine-Shift Transformer (VAST), the very first purely attention-free
shift-based video transformer. (c) We show that VAST significantly outperforms
recent state-of-the-art transformers on the most popular action recognition
benchmarks for the case of models with low computational and memory footprint.
Code will be made available.
