Space-time Mixing Attention for Video Transformer

TL;DR

This paper introduces a space-time mixing attention mechanism for Video Transformers that achieves high recognition accuracy with linear complexity in the number of frames, significantly reducing computational costs compared to existing models.

Contribution

The proposed model approximates full space-time attention with local temporal windows and efficient joint spatial-temporal mixing, enabling scalable and accurate video recognition.

Findings

Achieves high accuracy on popular video datasets.

Reduces computational complexity to linear in number of frames.

Provides a more efficient alternative to existing Video Transformers.

Abstract

This paper is on video recognition using Transformers. Very recent attempts in this area have demonstrated promising results in terms of recognition accuracy, yet they have been also shown to induce, in many cases, significant computational overheads due to the additional modelling of the temporal information. In this work, we propose a Video Transformer model the complexity of which scales linearly with the number of frames in the video sequence and hence induces no overhead compared to an image-based Transformer model. To achieve this, our model makes two approximations to the full space-time attention used in Video Transformers: (a) It restricts time attention to a local temporal window and capitalizes on the Transformer's depth to obtain full temporal coverage of the video sequence. (b) It uses efficient space-time mixing to attend jointly spatial and temporal locations without inducing any additional cost on top of a spatial-only attention model. We also show how to integrate 2 very lightweight mechanisms for global temporal-only attention which provide additional accuracy improvements at minimal computational cost. We demonstrate that our model produces very high recognition accuracy on the most popular video recognition datasets while at the same time being significantly more efficient than other Video Transformer models. Code will be made available.