  Domain shift is a well known problem where a model trained on a particular
domain (source) does not perform well when exposed to samples from a different
domain (target). Unsupervised methods that can adapt to domain shift are highly
desirable as they allow effective utilization of the source data without
requiring additional annotated training data from the target. Practically,
obtaining sufficient amount of annotated data from the target domain can be
both infeasible and extremely expensive. In this work, we address the domain
shift problem for the object detection task. Our approach relies on gradually
removing the domain shift between the source and the target domains. The key
ingredients to our approach are -- (a) mapping the source to the target domain
on pixel-level; (b) training a teacher network on the mapped source and the
unannotated target domain using adversarial feature alignment; and (c) finally
training a student network using the pseudo-labels obtained from the teacher.
Experimentally, when tested on challenging scenarios involving domain shift, we
consistently obtain significantly large performance gains over various recent
state of the art approaches.
