  Object detectors frequently encounter significant performance degradation
when confronted with domain gaps between collected data (source domain) and
data from real-world applications (target domain). To address this task,
numerous unsupervised domain adaptive detectors have been proposed, leveraging
carefully designed feature alignment techniques. However, these techniques
primarily align instance-level features in a class-agnostic manner, overlooking
the differences between extracted features from different categories, which
results in only limited improvement. Furthermore, the scope of current
alignment modules is often restricted to a limited batch of images, failing to
learn the entire dataset-level cues, thereby severely constraining the
detector's generalization ability to the target domain. To this end, we
introduce a strong DETR-based detector named Domain Adaptive detection
TRansformer (DATR) for unsupervised domain adaptation of object detection.
Firstly, we propose the Class-wise Prototypes Alignment (CPA) module, which
effectively aligns cross-domain features in a class-aware manner by bridging
the gap between object detection task and domain adaptation task. Then, the
designed Dataset-level Alignment Scheme (DAS) explicitly guides the detector to
achieve global representation and enhance inter-class distinguishability of
instance-level features across the entire dataset, which spans both domains, by
leveraging contrastive learning. Moreover, DATR incorporates a mean-teacher
based self-training framework, utilizing pseudo-labels generated by the teacher
model to further mitigate domain bias. Extensive experimental results
demonstrate superior performance and generalization capabilities of our
proposed DATR in multiple domain adaptation scenarios. Code is released at
https://github.com/h751410234/DATR.
