定向线索 - 意识到面部关系 代表头部姿势 估计通过变压器
概括
这项研究介绍了TokenHPE,一种基于变压器的新型头部姿势估计 (HPE) 方法. TokenHPE通过利用面部和定位关系来提高准确性,有效地应对诸如遮蔽和低光等挑战.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 头部姿势估计 (HPE) 对于人机交互和自动驾驶等应用至关重要.
- 现有的HPE方法面临着诸如阻塞,低照明和极端头部方向等挑战.
研究的目的:
- 开发一种新的,以关系为导向的方法,用于对强壮的头部姿势进行估计.
- 在现实场景中解决当前HPE技术的局限性.
主要方法:
- 提出了一个基于变压器的架构,TokenHPE,利用定向令牌编码面部区域.
- 该方法结合了头部图像中的内向和横向关系的见解.
- 一个以代币为指导的多损失函数旨在增强对区域相似性和关系的学习.
主要成果:
- 在三个具有挑战性的基准HPE数据集上,TokenHPE实现了最先进的性能.
- 拟议的方法在处理阻塞,低照度和极端头部姿势方面表现出有效性.
- 定性可视化证实了令牌学习方法的有效性.
结论:
- 开发的基于关系的方法显著提升了头部姿势估计能力.
- TokenHPE为面对现实世界挑战的实际HPE应用提供了强大的解决方案.
- 这种新的代币学习方法证明了它对于准确可靠的头部姿势预测是有效的.
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