面部表情识别-你只看一次-邻居协调注意力mamba:面部表情检测和分类基于邻居和坐标注意力机制
Cheng Peng1, Mingqi Sun2, Kun Zou1
1School of Computing, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
这项研究引入了面部表情识别 (FER) 的邻近坐标注意力mamba (NCAMamba),通过专注于关键面部区域来提高未对齐面部的准确性. 新模型显著提高了面部表情识别任务中平均精度 (mAP) 评分.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 面部表情识别 (FER) 对人机交互至关重要.
- 现有的方法与未对齐的面部图像作斗争.
- 对于对象检测任务,YOLOX框架是一个受欢迎的选择.
研究的目的:
- 开发一种新的特征提取器,以提高FER在非对齐面的性能.
- 将Mamba的功能与注意力机制集成在一起,以改善特征提取.
- 评估关于FER的基准数据集的拟议模型.
主要方法:
- 在YOLOX框架内引入邻近坐标注意力Mamba (NCAMamba) 作为特征提取器.
- NCAMamba 结合了Mamba来减少背景,邻里注意力来减少局部关系,并协调注意力来理解方向.
- 在RAF-DB和SFEW数据集上测试了FER-YOLO-NCAMamba模型.
主要成果:
- 与最先进的方法相比,FER-YOLO-NCAMamba模型实现了显著改善的平均平均精度 (mAP) 得分.
- 废除研究表明,邻近坐标注意 (NCA) 模块对视觉状态空间 (VSS) 的重要性更大.
- 通过Grad-CAM可视化,突出了鼻尖区域在表情识别中的关键作用.
结论:
- NCAMamba有效地提高了FER的性能,特别是在不整齐的面部.
- 关注特定的面部区域,如鼻尖,对于准确的表情识别至关重要.
- 这项研究为改善FER系统在现实世界的场景中提供了一个有希望的方向.
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