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  1. Home
  3. Temporal Gap-aware Attention Model For Temporal Action Proposal Generation.
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  3. Temporal Gap-aware Attention Model For Temporal Action Proposal Generation.

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Temporal Gap-Aware Attention Model for Temporal Action Proposal Generation.

Sorn Sooksatra1, Sitapa Watcharapinchai1

  • 1National Electronic and Computer Technology Center, National Science and Technology Development Agency, Khlong Nueng, Khlong Luang District, Pathum Thani 12120, Thailand.

Journal of Imaging
|December 27, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an attention mechanism to improve temporal action proposal generation in videos. The method enhances the segmentation of contiguous actions, achieving higher accuracy in boundary localization.

Keywords:
attention mechanismcontiguous action proposaltemporal action proposal generation

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Temporal action proposal generation aims to extract action instances from untrimmed videos.
  • Current methods face challenges in accurately segmenting contiguous action proposals with minimal temporal gaps.

Purpose of the Study:

  • To enhance the segmentation of contiguous action proposals in untrimmed videos.
  • To improve the localization accuracy of action boundaries by addressing temporal gaps.

Main Methods:

  • Incorporation of an attention mechanism to weigh proposal importance within contiguous groups.
  • Utilizing gap displacement between proposals to compute attention scores for boundary localization.

Main Results:

  • Significant improvement in the performance of short-duration and contiguous action proposals.
  • Achieved an average recall of 78.22% on ActivityNet v1.3 and Thumos 2014 datasets.
  • Outperformed a state-of-the-art boundary-based baseline.

    • Conclusions:

    • The proposed attention mechanism effectively addresses limitations in segmenting contiguous action proposals.
    • This approach offers a more accurate method for temporal action localization in videos.