  Despite growing interest in object detection, very few works address the
extremely practical problem of cross-domain robustness especially for
automative applications. In order to prevent drops in performance due to domain
shift, we introduce an unsupervised domain adaptation method built on the
foundation of faster-RCNN with two domain adaptation components addressing the
shift at the instance and image levels respectively and apply a consistency
regularization between them. We also introduce a family of adaptation layers
that leverage the squeeze excitation mechanism called SE Adaptors to improve
domain attention and thus improves performance without any prior requirement of
knowledge of the new target domain. Finally, we incorporate a center loss in
the instance and image level representations to improve the intra-class
variance. We report all results with Cityscapes as our source domain and Foggy
Cityscapes as the target domain outperforming previous baselines.
