Source-free domain-adaptive object detection is an interesting but scarcely addressed topic. It aims at adapting a source-pretrained detector to a distinct target domain without resorting to source data during adaptation. So far, there is no data augmentation scheme tailored to source-free domain-adaptive object detection. To this end, this paper presents a novel data augmentation approach that cuts out target image regions where the detector is confident, augments them along with their respective pseudo-labels, and joins them into a challenging target image to adapt the detector. As the source data is out of reach during adaptation, we implement our approach within a teacher-student learning paradigm to ensure that the model does not collapse during the adaptation procedure. We evaluated our approach on three adaptation benchmarks of traffic scenes, scoring new state-of-the-art on two of them.

翻译：无源域自适应目标检测是一个有趣但鲜有研究的话题。其目标是在不借助源域数据的情况下，将源域预训练的检测器适配到不同的目标域。迄今为止，尚无专门针对无源域自适应目标检测的数据增强方案。为此，本文提出了一种新颖的数据增强方法：该方法裁剪出检测器置信度较高的目标图像区域，将其与相应的伪标签一同增强，并组合成具有挑战性的目标图像以适配检测器。由于适配过程中无法获取源数据，我们在师生学习范式内实现了该方法，以确保模型在适配过程中不会崩溃。我们在三个交通场景的适配基准上评估了我们的方法，在其中两个基准上取得了新的最优性能。