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使用VTTransPose网络的一种高效和准确的二维人体姿势估计方法.

Rui Li1,2, Qi Li1, Shiqiang Yang3

  • 1School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xian, 710000, China.

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概括
此摘要是机器生成的。

VTTransPose模型通过提高效率和准确性来增强人类姿势估计. 这种基于变压器的方法提供了更好的性能,减少了计算需求和内存使用.

关键词:
功能融合的特点是:人类姿势估计估计变压器变压器变压器双胞胎注意力 双胞胎注意力

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 基于变压器的模型对于人类姿势估计非常受欢迎.
  • 现有的模型面临着高计算成本和局部细节识别的挑战.

研究的目的:

  • 提高基于变压器的人体姿势估计的效率和准确性.
  • 为了解决功能表示和网络性能方面的局限性.

主要方法:

  • 在TransPose中引入了双重注意模块以提高效率.
  • 开发了VTTransPose,将基本块替换为一个内部层特征融合V块.
  • 在COCO val2017和COCO test-dev2017数据集上验证了性能.

主要成果:

  • VTTransPose在COCO val2017上获得了76.5的AP分,在COCO test-dev2017上获得了73.6的AP分.
  • 与原来的TransPose相比,已经证明了0.4和0.2 AP的改进.
  • 减少了4.8G FLOPs,2M参数和~40%的内存使用量的计算需求.

结论:

  • 与原来的TransPose模型相比,VTTransPose提供了更高的精度和效率.
  • 该模型更轻量化,适用于资源有限的环境.