跨类和跨域语义增强用于域泛化
概括
本研究引入了类间和域间语义增强 (CDSA),以改善域泛化. 通过新的语义增强技术,CDSA通过增加源域多样性来提高未见数据上的模型性能.
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:
- 域泛化旨在创建适用于新,未见的数据域的模型.
- 数据增强,特别是语义层次的方法,提高了域泛化,但往往缺乏源域多样性.
- 现有的语义增强技术在类内和域内采样方向,限制了它们的有效性.
研究的目的:
- 提出一种新的方法,即跨类和跨域语义增强 (CDSA),以增强域名泛化.
- 解决当前语义级数据增强方法中源域多样性不足的局限性.
主要方法:
- 介绍了CrossSmooth,这是一种基于采样的方法,可以从类间变化中生成语义方向.
- 开发了CrossVariance以通过采样这些类间语义方向来捕捉各种域风格.
- 实现了CDSA用于域泛化任务.
主要成果:
- 在四个基准数据集上证明了CDSA的显著有效性:数字-DG,PACS,Office-Home和DomainNet.
- 在对语义细分数据集 (GTAV,SYNTHIA,Cityscapes,Mapillary和BDDS) 进行CDSA验证时取得了实质性的改进.
结论:
- 通过增加源域多样性,CDSA有效地提高了域泛化.
- 拟议的方法在各种计算机视觉任务中显示了广泛的适用性和显著的性能增长.
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