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%.

本研究提出了一种基于梯度分离的堆叠互补损失（SCL）方法，该方法使用检测损失作为主要目标，并在不同的网络阶段引入几个辅助损失以学习更具判别性的表示，用于非监督领域自适应目标检测。实验表明该方法在多个数据集上表现优异，尤其是在 Cityscapes 到 FoggyCityscapes 的领域偏移条件下具有显著增强性能，与现有方法相比平均提高了 3.6%。