在域自适应语义细分中处理开放式噪声和新目标识别
IEEE transactions on pattern analysis and machine intelligence
|October 11, 2023
概括
这项研究引入了一个简单的噪音过渡矩阵 (SimT),以解决带有噪音伪标签的域自适应语义细分. SimT有效地模拟混合噪声,使得更好的新型目标识别和改进的细分性能.
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:
- 域自适应 (DA) 语义细分面临来自黑盒模型的杂伪标签目标数据的挑战.
- 由于域间隙和标签转移,现有的方法与混合的封闭式和开放式标签噪声作斗争.
研究的目的:
- 提出一种新的方法,简单噪声过渡矩阵 (SimT),用于在DA语义细分中建模混合噪声分布.
- 利用SimT来处理开放式标签噪声,并实现新的目标识别.
- 提高对目标域数据的细分模型的概括能力.
主要方法:
- 开发了一个简单的噪声过渡矩阵 (SimT) 来建模DA语义细分中的混合噪声分布.
- 设计了四个互补的调节器 (体积调节,引导,凸 garant,语义约束) 来估计SimT.
- 拟议的封闭对开放标签校正 (C2OLC) 和语义关系 (SR) 损失,用于开放集类识别.
主要成果:
- 拟议的SimT有效地模拟了混合的封闭式和开放式标签噪声.
- 通过纠正伪标签中的噪音,SimT可以准确地识别新型目标.
- 实验结果表明,SimT可以提高现有DA方法的性能,无论是封闭集和开放集类.
结论:
- 简单的噪音过渡矩阵 (SimT) 为带有噪音的伪标签的域自适应语义细分提供了强大的解决方案.
- SimT可以集成到现有的DA框架中,以提高性能,特别是在具有开放式噪声和新目标识别的场景中.
- 拟议的调节器和损失有效地解决了混合噪声分布的复杂性,并促进了更好的泛化.
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