  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.
