Papers › Space-time Mixing Attention for Video Transformer

Space-time Mixing Attention for Video Transformer

10 Jun 2021NeurIPS 2021 12arXiv:2106.05968archive 2025-07-28

Adrian Bulat, Juan-Manuel Perez-Rua, Swathikiran Sudhakaran, Brais Martinez, Georgios Tzimiropoulos

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.

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Tasks

Action ClassificationAction RecognitionAction Recognition In VideosVideo Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Classification Kinetics-600 XViT (x16) Top-1 Accuracy 84.5 #32 of 65 Archive leaderboard report
Action Classification Kinetics-600 XViT (x16) Top-5 Accuracy 96.3 #32 of 65 Archive leaderboard report
Action Recognition Something-Something V2 X-Vit (x16) GFLOPs 850x1 #68 of 123 Archive leaderboard report
Action Recognition Something-Something V2 X-Vit (x16) Parameters N/A #68 of 123 Archive leaderboard report
Action Recognition Something-Something V2 X-Vit (x16) Top-1 Accuracy 67.2 #68 of 123 Archive leaderboard report
Action Recognition Something-Something V2 X-Vit (x16) Top-5 Accuracy 90.8 #68 of 123 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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