  Training (source) domain bias affects state-of-the-art object detectors, such
as Faster R-CNN, when applied to new (target) domains. To alleviate this
problem, researchers proposed various domain adaptation methods to improve
object detection results in the cross-domain setting, e.g. by translating
images with ground-truth labels from the source domain to the target domain
using Cycle-GAN. On top of combining Cycle-GAN transformations and self-paced
learning in a smart and efficient way, in this paper, we propose a novel
self-paced algorithm that learns from easy to hard. Our method is simple and
effective, without any overhead during inference. It uses only pseudo-labels
for samples taken from the target domain, i.e. the domain adaptation is
unsupervised. We conduct experiments on four cross-domain benchmarks, showing
better results than the state of the art. We also perform an ablation study
demonstrating the utility of each component in our framework. Additionally, we
study the applicability of our framework to other object detectors.
Furthermore, we compare our difficulty measure with other measures from the
related literature, proving that it yields superior results and that it
correlates well with the performance metric.
