不确定性意识图表对比融合网络用于多式生理信号的多式生理信号情绪识别
Guangqiang Li1, Ning Chen1, Hongqing Zhu1
1School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
概括
这项研究引入了一个不确定性意识图对比融合网络 (UAGCFNet),用于使用生理信号识别情绪. 这种新的方法通过解决传感器间连接的不确定性,有效地融合多式联网数据,优于现有方法.
科学领域:
- 多模式机器学习是多模式机器学习.
- 情感计算是一种情感计算.
- 信号处理 信号处理
背景情况:
- 图形神经网络 (GNN) 用于从生理信号识别情绪,但与非静止性和噪音作斗争.
- 现有的多模式融合模型不能充分解决传感器间连接的不确定性和由此产生的单模式偏差.
研究的目的:
- 提出一个不确定性感知图对比融合网络 (UAGCFNet),用于在情感识别中有效的多模式生理信号融合.
- 为了解决跨模态语义差距的不确定性和单模态偏差问题.
主要方法:
- 开发了一个不确定性意识图形卷积网络 (UAGCN) 来量化传感器间连接的不确定性,并提取不确定性意识图形表示.
- 设计了一个过渡对比融合 (TCF) 模块,集成危机交叉注意力 (CCA) 和过渡对比学习 (TCL) 以实现强大的多式联结.
主要成果:
- 在DEAP,DREAMER和MPED数据集上,UAGCFNet模型表现出优于最先进的多式联络融合模型 (SOTA) 的性能.
- 用更少的参数和更低的计算复杂度实现更好的结果.
- 有效地消除了先前方法固有的单模偏差问题.
结论:
- 拟议的UAGCFNet有效地融合了多模式的生理信号,以增强情绪识别.
- 不确定性意识图形卷积网络和过渡性对比融合模块对于性能增长至关重要.
- 该模型成功地减轻了单模式偏差,提高了聚变效率.
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