Efficient Attention-free Video Shift Transformers

TL;DR

This paper introduces VAST, an attention-free video transformer using shift operations, achieving high efficiency and accuracy in video recognition tasks, outperforming existing models with lower computational costs.

Contribution

The paper presents the first attention-free shift-based video transformer, VAST, that approximates transformer operations and outperforms state-of-the-art models in efficiency and accuracy.

Findings

VAST outperforms recent transformers on action recognition benchmarks.

The Affine-Shift block achieves high accuracy with low computational cost.

VAST is the first purely shift-based video transformer.

Abstract

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.