Object detectors do not work well when domains largely differ between training and testing data. To overcome this domain gap in object detection without requiring expensive annotations, we consider two problem settings: semi-supervised domain generalizable object detection (SS-DGOD) and weakly-supervised DGOD (WS-DGOD). In contrast to the conventional domain generalization for object detection that requires labeled data from multiple domains, SS-DGOD and WS-DGOD require labeled data only from one domain and unlabeled or weakly-labeled data from multiple domains for training. In this paper, we show that object detectors can be effectively trained on the two settings with the same Mean Teacher learning framework, where a student network is trained with pseudo-labels output from a teacher on the unlabeled or weakly-labeled data. We provide novel interpretations of why the Mean Teacher learning framework works well on the two settings in terms of the relationships between the generalization gap and flat minima in parameter space. On the basis of the interpretations, we also propose incorporating a simple regularization method into the Mean Teacher learning framework to find flatter minima. The experimental results demonstrate that the regularization leads to flatter minima and boosts the performance of the detectors trained with the Mean Teacher learning framework on the two settings. They also indicate that those detectors significantly outperform the state-of-the-art methods.

翻译：目标检测器在训练数据和测试数据领域差异较大时性能不佳。为解决目标检测中的这一领域差距且无需昂贵标注，本研究考虑了两种问题设置：半监督领域泛化目标检测（SS-DGOD）和弱监督领域泛化目标检测（WS-DGOD）。与需要多领域标注数据的传统目标检测领域泛化不同，SS-DGOD 和 WS-DGOD 仅需单一领域的标注数据，以及来自多个领域的未标注或弱标注数据进行训练。本文证明，在两种设置下均可通过相同的均值教师学习框架有效训练目标检测器，其中学生网络利用教师网络对未标注或弱标注数据输出的伪标签进行训练。我们从参数空间中泛化差距与平坦极小值之间的关系出发，对均值教师学习框架在两种设置下表现优异的原因提出了全新解释。基于这些解释，我们进一步提出在均值教师学习框架中融入简单正则化方法，以寻找更平坦的极小值。实验结果表明，该正则化方法能引导模型收敛至更平坦极小值，并提升基于均值教师学习框架训练的检测器在两种设置下的性能。这些结果同时表明，上述检测器显著优于现有最优方法。