  Unsupervised domain adaptive object detection aims to learn a robust detector
in the domain shift circumstance, where the training (source) domain is
label-rich with bounding box annotations, while the testing (target) domain is
label-agnostic and the feature distributions between training and testing
domains are dissimilar or even totally different. In this paper, we propose a
gradient detach based stacked complementary losses (SCL) method that uses
detection losses as the primary objective, and cuts in several auxiliary losses
in different network stages accompanying with gradient detach training to learn
more discriminative representations. We argue that the prior methods mainly
leverage more loss functions for training but ignore the interaction of
different losses and also the compatible training strategy (gradient detach
updating in our work). Thus, our proposed method is a more syncretic adaptation
learning process. We conduct comprehensive experiments on seven datasets, the
results demonstrate that our method performs favorably better than the
state-of-the-art methods by a significant margin. For instance, from Cityscapes
to FoggyCityscapes, we achieve 37.9% mAP, outperforming the previous art
Strong-Weak by 3.6%.
