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.

我们提出了Class-Aware Teacher（CAT）来解决域自适应设置中的类别偏差问题，并利用我们的Inter-Class Relation module（ICRm）来近似类别关系，减少模型内的偏差，从而在高相关类别上使用增强方法以提高少数类别的性能，同时对多数类别影响最小。通过在不同数据集上的实验证明，我们的方法能够解决域自适应设置中的类别偏差问题，在Cityscapes到Foggy Cityscapes数据集上，我们实现了52.5 mAP的性能，显著改进了现有最先进方法的51.2 mAP。

利用类间动态进行领域自适应物体检测