Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domain. However, for high-stakes applications (e.g. autonomous driving), it is common to have a modest amount of human-labeled real data in addition to plentiful auto-labeled source data (e.g. from a driving simulator). We study this setting of supervised sim2real DA applied to 2D object detection. We propose Domain Translation via Conditional Alignment and Reweighting (CARE) a novel algorithm that systematically exploits target labels to explicitly close the sim2real appearance and content gaps. We present an analytical justification of our algorithm and demonstrate strong gains over competing methods on standard benchmarks.

翻译：Sim2Real域适应（DA）研究聚焦于从带标签的合成源域适应至无标签或稀疏标签的真实目标域这一约束场景。然而，在高风险应用场景（如自动驾驶）中，除大量自动标注的源域数据（例如来自驾驶模拟器）外，通常还拥有少量人工标注的真实数据。本研究针对应用于二维目标检测的有监督Sim2Real域适应场景进行探索。我们提出"基于条件对齐与重加权的域迁移"（CARE）这一新颖算法，该算法系统性地利用目标域标签显式弥合Sim2Real在表观与内容层面的差异。我们给出了算法的理论分析依据，并在标准基准测试中展示了相较于现有方法的显著性能提升。