通过对比热身和复杂性意识自我训练进行跨域面部表情识别
概括
这项研究引入了一个新的框架,用于无监督的跨域面部表情识别 (FER). 该方法通过使用对比式学习和复杂性意识自我训练来改善知识传递和目标语义学习.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 无监督的跨域面部表情识别 (FER) 将知识从标记源转移到未标记的目标域.
- 由于缺乏标签,现有的方法难以利用目标域语义.
- 降低域差异是FER转移学习的一个关键挑战.
研究的目的:
- 提出一个新的框架,对比热身和复杂性意识自我训练 (CWCST),用于有效的无监督跨领域FER.
- 共同促进源知识转移和目标语义学习.
- 克服现有方法在探索目标域语义信息方面的局限性.
主要方法:
- 使用特征,势头特征和类别中心来学习歧视性表示并减少域差距的对比热身策略.
- 基于预测的标签选择模块进行复杂性意识的自我训练,以可靠的伪标签生成.
- 源目标联合培训,有效利用源知识和目标语义信息.
主要成果:
- 与七个数据库的各种基线相比,CWCST框架表现出卓越的性能.
- 提出的方法有效地缩小了域差距,并产生了更准确的目标伪标签.
- 复杂性意识的自我训练组件成功地处理杂的伪标签,以实现强大的目标语义探索.
结论:
- 该CWCST框架为无监督跨域FER提供了显著的进步.
- 对比热身和复杂性意识自我训练的联合应用增强了知识传递和语义学习.
- 该框架的模块化使其易于集成到现有方法中,从而产生一致的改进.
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