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.

训练（源）域偏差在应用于新的（目标）域时会影响最新的对象检测器，例如Faster R-CNN。为了缓解这个问题，研究人员提出了各种域自适应方法，以改善跨域设置中的对象检测结果，例如，使用Cycle-GAN将带有地面标签的图像从源域转换为目标域。在将Cycle-GAN变换和自定进度的学习以一种聪明而有效的方式结合在一起的基础上，本文提出了一种新颖的自定进度的算法，该算法从易学到难学。我们的方法简单有效，在推理过程中没有任何开销。对于从目标域获取的样本，它仅使用伪标记，即域自适应不受监督。我们在四个跨域基准测试中进行了实验，显示出比现有技术更好的结果。我们还将进行消融研究，以证明我们框架中每个组件的效用。此外，我们研究了我们的框架对其他物体检测器的适用性。此外，我们将难度指标与相关文献中的其他指标进行了比较，证明了它产生了更好的结果，并且与绩效指标具有很好的相关性。