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通过LoRA增强的DinoV2和交互式时空建模进行微表达式识别.

Meng Wang1, Xueping Tang1, Bing Wang1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, 727 South Jingming Road, Kunming 650500, China.

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概括
此摘要是机器生成的。

本研究介绍了一种使用频率分析和图形网络的微表达式识别 (MER) 架构. 该方法增强了微妙的面部运动检测,在基准数据集上取得了最先进的结果.

关键词:
富里叶变换是什么意思 富里叶变换洛拉 (LoRA) 是一个名字.图表注意力网络 图表注意力网络识别微表情的功能时间空间建模.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 生物识别信息 生物识别信息

背景情况:

  • 微表达式识别 (MER) 面临挑战,因为时间短,强度低,空间频率模式复杂.
  • 现有的MER方法通常需要大量的预处理和大量的计算资源.
  • 微妙的面部纹理变化很难被有效地捕捉和分析.

研究的目的:

  • 引入一种新的MER架构,可以提高准确性,同时降低计算成本.
  • 集成频域转换和基于图形的时间建模,以实现高效的MER.
  • 通过适应空间频率分解来增强对微妙面部运动的敏感性.

主要方法:

  • 用LoRA (低级调整) 微调一个大型特征提取模型,以提高计算效率.
  • 实施一个空间频率适应 (SFA) 模块来分解和动态权重空间频率.
  • 使用动态图表注意时间 (DGAT) 网络,将图表注意网络和LSTM结合起来进行时间建模.

主要成果:

  • 拟议的MER架构在SAMM,CASME II和SMIC数据集上实现了卓越的性能.
  • 在SAMM 5类设置中,该方法产生了81.16%的未加权F1得分 (UF1) 和85.37%的未加权平均回忆 (UAR).
  • 该方法在UF1和UAR方面表现优于现有方法,在UF1和UAR方面表现显著.

结论:

  • 新的MER架构有效地解决了短时间,低强度和空间频率变化的挑战.
  • 集成频域分析和基于图形的时间建模,为MER提供了高效和准确的方法.
  • 拟议的方法显示了对现实世界微表达式分析应用的巨大潜力.