  Recent deep learning methods for object detection rely on a large amount of
bounding box annotations. Collecting these annotations is laborious and costly,
yet supervised models do not generalize well when testing on images from a
different distribution. Domain adaptation provides a solution by adapting
existing labels to the target testing data. However, a large gap between
domains could make adaptation a challenging task, which leads to unstable
training processes and sub-optimal results. In this paper, we propose to bridge
the domain gap with an intermediate domain and progressively solve easier
adaptation subtasks. This intermediate domain is constructed by translating the
source images to mimic the ones in the target domain. To tackle the
domain-shift problem, we adopt adversarial learning to align distributions at
the feature level. In addition, a weighted task loss is applied to deal with
unbalanced image quality in the intermediate domain. Experimental results show
that our method performs favorably against the state-of-the-art method in terms
of the performance on the target domain.
