  We propose a domain adaptation approach for object detection. We introduce a
two-step method: the first step makes the detector robust to low-level
differences and the second step adapts the classifiers to changes in the
high-level features. For the first step, we use a style transfer method for
pixel-adaptation of source images to the target domain. We find that enforcing
low distance in the high-level features of the object detector between the
style transferred images and the source images improves the performance in the
target domain. For the second step, we propose a robust pseudo labelling
approach to reduce the noise in both positive and negative sampling.
Experimental evaluation is performed using the detector SSD300 on PASCAL VOC
extended with the dataset proposed in arxiv:1803.11365 where the target domain
images are of different styles. Our approach significantly improves the
state-of-the-art performance in this benchmark.
