  Object detection typically assumes that training and test data are drawn from
an identical distribution, which, however, does not always hold in practice.
Such a distribution mismatch will lead to a significant performance drop. In
this work, we aim to improve the cross-domain robustness of object detection.
We tackle the domain shift on two levels: 1) the image-level shift, such as
image style, illumination, etc, and 2) the instance-level shift, such as object
appearance, size, etc. We build our approach based on the recent
state-of-the-art Faster R-CNN model, and design two domain adaptation
components, on image level and instance level, to reduce the domain
discrepancy. The two domain adaptation components are based on H-divergence
theory, and are implemented by learning a domain classifier in adversarial
training manner. The domain classifiers on different levels are further
reinforced with a consistency regularization to learn a domain-invariant region
proposal network (RPN) in the Faster R-CNN model. We evaluate our newly
proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K,
etc. The results demonstrate the effectiveness of our proposed approach for
robust object detection in various domain shift scenarios.
