用CARE弥合Sim2Real差距：有条件对齐和重新加权的监督检测自适应
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
摘要：
Sim2Real领域自适应（DA）研究的重点是从标记的合成源域到未标记或稀疏标记的真实目标域的自适应约束设置。然而，对于高风险应用（例如，自动驾驶），除了大量的自动标记的源数据（例如，来自驾驶模拟器）之外，通常还具有适量的人类标记的真实数据。我们研究了这种设置的监督sim2real DA应用到二维目标检测。我们提出了域翻译通过条件对齐和重新加权（CARE）一种新的算法，系统地利用目标标签显式地关闭sim2real外观和内容的差距。我们提出了一个分析的理由，我们的算法，并证明了强大的收益在标准基准的竞争方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
