  Domain adaptive object detection aims to adapt detection models to domains
where annotated data is unavailable. Existing methods have been proposed to
address the domain gap using the semi-supervised student-teacher framework.
However, a fundamental issue arises from the class imbalance in the labelled
training set, which can result in inaccurate pseudo-labels. The relationship
between classes, especially where one class is a majority and the other
minority, has a large impact on class bias. We propose Class-Aware Teacher
(CAT) to address the class bias issue in the domain adaptation setting. In our
work, we approximate the class relationships with our Inter-Class Relation
module (ICRm) and exploit it to reduce the bias within the model. In this way,
we are able to apply augmentations to highly related classes, both inter- and
intra-domain, to boost the performance of minority classes while having minimal
impact on majority classes. We further reduce the bias by implementing a
class-relation weight to our classification loss. Experiments conducted on
various datasets and ablation studies show that our method is able to address
the class bias in the domain adaptation setting. On the Cityscapes to Foggy
Cityscapes dataset, we attained a 52.5 mAP, a substantial improvement over the
51.2 mAP achieved by the state-of-the-art method.
