超级自动化多专家网络跨域几拍面部表情识别
概括
这项研究引入了一种用于跨领域短拍面部表情识别 (CF-FER) 的新型网络. 拟议的多专家网络 (HSM-Net) 通过解决数据不平衡来改善可转移的表示.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 跨域短拍面部表情识别 (CF-FER) 旨在使用来自新域的有限数据识别新表情.
- 现有的CF-FER方法经常与不平衡的数据集扎,并且无法捕捉欧几里德空间中面部表情的等级性质.
- 这导致了低于最佳的可转移表示.
研究的目的:
- 为改进CF-FER提出了一个新型网络,即超级自动化多专家网络 (HSM-Net).
- 解决欧几里德空间嵌入在处理不平衡表达类别和样本困难方面的局限性.
- 为了增强对等级面部表情关系的建模,并获得更多可转移的特征.
主要方法:
- 开发了HSM-Net,在超标空间内具有多个专家混合 (MoE) 层.
- 实施了使用自蒸的协作培训方法,专家专注于表达类别的子集.
- 引入了超标自动学习 (HSL) 策略,以自适应地训练模型,从容易到困难的样本,减轻数据不平衡问题.
主要成果:
- 在HSM-Net中,可以有效地模拟层次化的面部表情关系.
- 该方法实现了高度可转移的特征空间,优于现有的最先进的方法.
- 在实验室和现实数据集上的实验验证实了拟议方法的优越性.
结论:
- 拟议的HSM-Net在跨域短拍面部表情识别方面取得了重大进展.
- 通过利用超标几何学和自动学习,网络有效地处理数据不平衡,并增强功能可转移性.
- 该方法在复杂的面部表情识别任务上表现出强大的性能.
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