CDGT:从面部视频中构建各种图形变压器来识别情绪
Dongliang Chen1, Guihua Wen1, Huihui Li2
1School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China.
概括
这项研究介绍了一种新的多样化图形变压器 (CDGT),用于在具有挑战性的野生视频中准确识别面部表情. CDGT有效地捕捉空间和时间特征,优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 从动态视频中识别面部表情是具有挑战性的,原因是姿势变化,遮蔽和微妙的情绪变化.
- 现有的变压器方法很难专注于局部的时空特征,这对于野外表达分析至关重要.
研究的目的:
- 提出一种新的方法,多种图形变压器 (CDGT),用于从野生面部视频中高效准确地识别情绪.
- 增强局部表达相关结构在空间和时间领域的建模.
主要方法:
- 拟议的CDGT方法将多种图形结构纳入变压器架构中.
- 它具有空间双图形变压器,具有双图形受限的注意力,用于本地空间令牌分析.
- 它还包括一个使用形图限制自我注意力进行微妙的动态情感建模的时间形图转换器.
主要成果:
- 空间双图形变压器有效地处理呈现变化和部分遮蔽.
- 时间的形图形变压器捕捉到微妙的动态情绪变化.
- 广泛的实验证明了CDGT在野外视频数据集上比最先进的方法具有更高的性能.
结论:
- CDGT方法在复杂的现实视频中识别面部表情方面取得了重大进展.
- 将多样化的图形结构集成到变压器中,提高了捕捉情感识别关键本地化时空特征的能力.
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