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基于多头注意力,融合和相互作用的凝视估计网络

Changli Li¹, Fangfang Li¹, Kao Zhang¹

¹School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Sensors (Basel, Switzerland)

|April 28, 2025

概括

这项研究引入了一种改进的目光估计网络,该网络使用多头注意力有效地融合了眼睛和面部特征. 这种新的方法通过更好地利用特征相关性来提高确定视觉注意力的准确性.

科学领域:

计算机视觉计算机视觉
人与计算机的交互
人工智能的人工智能

背景情况:

视线位置是人类视觉注意力的关键指标,对于各种应用至关重要.
当前的目光估计模型通常会单独处理眼睛和面部数据,从而限制了特征相关性的利用.

研究的目的:

开发一个新的目光估计网络,更有效地整合面部和眼睛特征.
通过利用特征相关性来提高目光估计的准确性和稳定性.

主要方法:

提出了一个新的凝视估计网络,整合了多头注意力,融合和交互策略.
雇佣了多头注意力和道注意力,以从两只眼睛的保险丝特征.
引入了面部和眼睛交互模块,并修改了卷积块注意力模块 (CBAM) 以最小的聚合和快捷连接.

主要成果:

与现有方法相比,拟议的网络表现出优越的性能.
在Gaze360,MPIIFaceGaze和EYEDIAP数据集上的实验验验证了该方法的有效性.
面部和眼睛特征的整合显著改善了凝视估计的准确性.

结论:

开发的凝视估计网络有效地融合了多模式特征,性能优于以前的方法.
新的注意力和交互机制增强了模型捕捉微妙的目光线索的能力.
这项工作在准确可靠的目光估计技术方面取得了重大进展.

关键词:

卷积神经网络是一种卷积神经网络.功能互动功能互动凝视的估计估计.多头注意力多头注意力

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