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一种基于坐标识别面部表情的创新邻居注意力机制

Cheng Peng¹, Bohao Li², Kun Zou¹

¹School of Computing, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.

Sensors (Basel, Switzerland)

|November 27, 2024

概括

一个新的特征提取器,基于坐标的邻里注意力机制 (CNAM),通过更好地区分特征集群来改善面部表情识别 (FER). CNAM提高了基准数据集的性能,在某些方面表现优于最先进的方法.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
人工智能的人工智能

背景情况:

面部表情识别 (FER) 是计算机视觉中的一个具有挑战性的任务.
现有的方法往往难以捕捉复杂的特征关系.
对于精确的FER系统,需要强大的特征提取是至关重要的.

研究的目的:

为了引入一个新的特征提取器,以坐标为基础的邻里注意力机制 (CNAM),为FER.
评估CNAM在提高特征表示的歧视力方面的有效性.
将CNAM的业绩与基准FER数据集的最先进方法进行比较.

主要方法:

开发了CNAM,集成了指向特征关系的协调注意力 (CA) 和局部特征相互作用的邻里注意力 (NA).
整合了一个使用多层感知器 (MLP) 的通道注意力机制.
将CNAM应用于四个基准数据集:RAF-DB,AffectNet (7类和8类) 和CK+.

主要成果:

在高维特征向量中,CNAM有效地减少集群内部距离并增加集群间距离.
在AffectNet (7类) 和CK+数据集上实现了最先进的 (SOTA) 性能.
在RAF-DB和AffectNet (8类) 数据集上表现出顶级性能,与其他SOTA方法具有竞争力.

结论:

拟议的CNAM模块是用于面部表情识别的高效特征提取器.
CNAM显著提高了特征表示的质量,从而提高了FER的准确性.
与现有方法相比,CNAM代表了FER的有价值的进步,提供了与现有方法相比具有竞争力或优异的性能.

关键词:

协调注意力,协调注意力.深度学习是一种深度学习.面部表情识别面部表情识别邻居注意力模型

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