Object detectors do not work well when domains largely differ between
training and testing data. To solve this problem, domain generalization
approaches, which require training data with ground-truth labels from multiple
domains, have been proposed. However, it is time-consuming and labor-intensive
to collect those data for object detection because not only class labels but
also bounding boxes must be annotated. To overcome the problem of domain gap in
object detection without requiring expensive annotations, we propose to
consider two new 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. We show that object detectors can be effectively trained on the
proposed settings with the same student-teacher learning framework, where a
student network is trained with pseudo labels output from a teacher on the
unlabeled or weakly-labeled data. The experimental results demonstrate that the
object detectors trained on the proposed settings significantly outperform
baseline detectors trained on one labeled domain data and perform comparably to
or better than those trained on unsupervised domain adaptation (UDA) settings,
while ours do not use target domain data for training in contrast to UDA.

在目标检测中，提出了半监督领域通用目标检测（SS-DGOD）和弱监督领域通用目标检测（WS-DGOD）两种新的问题设置，利用师生学习框架在仅使用来自一个领域标记数据和来自多个领域的无标记或弱标记数据进行训练，并证明了在此设置下训练的目标检测器明显优于基线检测器，并与无监督领域自适应（UDA）设置下训练的目标检测器性能相媲美或更好，同时与 UDA 相比，不需要使用目标领域数据进行训练。