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WLiT: Windows and Linear Transformer for Video Action Recognition.

Ruoxi Sun1,2, Tianzhao Zhang1,3, Yong Wan4

  • 1Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.

Sensors (Basel, Switzerland)
|February 11, 2023
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Summary

We introduce the Windows and Linear Transformer (WLiT), an efficient model for video action recognition. WLiT significantly reduces computational complexity while enhancing recognition accuracy by combining spatial-window and linear attention mechanisms.

Keywords:
Spatial-Windows attentionaction recognitionlinear attentionself-attentiontransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Transformer models have advanced video understanding but suffer from high computational complexity.
  • Existing methods like windowed or down-sampled attention reduce complexity but have limitations.
  • Further optimization is needed for efficient and accurate video action recognition.

Purpose of the Study:

  • To develop an efficient video action recognition model with reduced computational complexity.
  • To improve recognition accuracy by addressing limitations of previous attention mechanisms.
  • To introduce the Windows and Linear Transformer (WLiT) model.

Main Methods:

  • Implemented Spatial-Windows attention by dividing feature maps into spatial windows for localized attention.
  • Incorporated Linear attention along the channel dimension to capture global spatiotemporal information.
  • Combined these mechanisms in the Windows and Linear Transformer (WLiT) architecture.

Main Results:

  • WLiT achieved state-of-the-art (SOTA) level accuracy on multiple datasets (SSV2, K400, UCF101, HMDB51).
  • Reduced computational complexity by 28% on SSV2 and 49% on K400 and other datasets.
  • Improved recognition accuracy by 1.6% on SSV2 compared to SOTA methods.

Conclusions:

  • The WLiT model offers an effective solution for efficient video action recognition.
  • The combination of Spatial-Windows and Linear attention successfully balances computational efficiency and accuracy.
  • WLiT demonstrates superior performance across diverse video action recognition benchmarks.