多分支卷积神经网络具有交叉注意力机制,用于情绪识别和认知
Fei Yan1, Zekai Guo1, Abdullah M Iliyasu2,3
1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
Scientific reports
|February 1, 2025
概括
这项研究引入了一种新的多分支卷积神经网络与交叉注意力 (MCNN-CA),用于增强情绪识别. 该MCNN-CA模型使用多式联络数据准确地识别情绪,优于现有方法.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 神经科学是一个神经科学.
背景情况:
- 情感识别在教育,营销和医疗保健方面具有广泛的应用.
- 从多式联运数据中准确地识别情绪仍然是一个挑战.
- 现有的模型经常在有效的特征提取和融合方面扎.
研究的目的:
- 为准确的情感识别提出一个新的多分支卷积神经网络与交叉注意力 (MCNN-CA).
- 从多式联运数据中自动提取特征,并合并各种特征地图.
- 改进不同数据模式内和不同数据模式之间的特征的相互关联.
主要方法:
- 开发了一个MCNN-CA模型,利用各种卷积神经网络进行特征提取.
- 实现了一个具有通道效率注意力机制的功能融合模块.
- 对EEG情感识别数据集 (SEED,SEED-IV) 和多式EEG文本数据 (ZuCo) 的模型进行了评估.
主要成果:
- 该MCNN-CA模型在情绪识别任务中表现出卓越的表现.
- 与当代方法相比,实现了高准确度,精度,回忆和F1得分.
- 证实了单模式和交叉模式特征的有效融合.
结论:
- 拟议的MCNN-CA模型为情绪识别提供了强大而准确的解决方案.
- 交叉注意力机制和功能融合模块是其增强性能的关键.
- 这种方法对推进情感计算和人机交互具有重大潜力.
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