  The recent detection transformer (DETR) simplifies the object detection
pipeline by removing hand-crafted designs and hyperparameters as employed in
conventional two-stage object detectors. However, how to leverage the simple
yet effective DETR architecture in domain adaptive object detection is largely
neglected. Inspired by the unique DETR attention mechanisms, we design DA-DETR,
a domain adaptive object detection transformer that introduces information
fusion for effective transfer from a labeled source domain to an unlabeled
target domain. DA-DETR introduces a novel CNN-Transformer Blender (CTBlender)
that fuses the CNN features and Transformer features ingeniously for effective
feature alignment and knowledge transfer across domains. Specifically,
CTBlender employs the Transformer features to modulate the CNN features across
multiple scales where the high-level semantic information and the low-level
spatial information are fused for accurate object identification and
localization. Extensive experiments show that DA-DETR achieves superior
detection performance consistently across multiple widely adopted domain
adaptation benchmarks.
