Cross-domain object detection is more challenging than object classification
since multiple objects exist in an image and the location of each object is
unknown in the unlabeled target domain. As a result, when we adapt features of
different objects to enhance the transferability of the detector, the features
of the foreground and the background are easy to be confused, which may hurt
the discriminability of the detector. Besides, previous methods focused on
category adaptation but ignored another important part for object detection,
i.e., the adaptation on bounding box regression. To this end, we propose
D-adapt, namely Decoupled Adaptation, to decouple the adversarial adaptation
and the training of the detector. Besides, we fill the blank of regression
domain adaptation in object detection by introducing a bounding box adaptor.
Experiments show that D-adapt achieves state-of-the-art results on four
cross-domain object detection tasks and yields 17% and 21% relative improvement
on benchmark datasets Clipart1k and Comic2k in particular.

本文提出基于 D-adapt 的跨域目标检测方法，通过对对抗性适应性和检测器训练的分离来消除前景和背景特征的混淆，并通过引入边界框适配器来填补物体检测中回归域适应的空白， 在四项跨领域目标检测任务中取得了最先进的结果，并在特别是 Clipart 1k 和 Comic 2k 这两个基准数据集上相对提高了 17％和 21％。