  Unsupervised domain adaptation object detection (UDAOD) research on Detection
Transformer(DETR) mainly focuses on feature alignment and existing methods can
be divided into two kinds, each of which has its unresolved issues. One-stage
feature alignment methods can easily lead to performance fluctuation and
training stagnation. Two-stage feature alignment method based on mean teacher
comprises a pretraining stage followed by a self-training stage, each facing
problems in obtaining reliable pretrained model and achieving consistent
performance gains. Methods mentioned above have not yet explore how to utilize
the third related domain such as target-like domain to assist adaptation. To
address these issues, we propose a two-stage framework named MTM, i.e. Mean
Teacher-DETR with Masked Feature Alignment. In the pretraining stage, we
utilize labeled target-like images produced by image style transfer to avoid
performance fluctuation. In the self-training stage, we leverage unlabeled
target images by pseudo labels based on mean teacher and propose a module
called Object Queries Knowledge Transfer (OQKT) to ensure consistent
performance gains of the student model. Most importantly, we propose masked
feature alignment methods including Masked Domain Query-based Feature Alignment
(MDQFA) and Masked Token-wise Feature Alignment (MTWFA) to alleviate domain
shift in a more robust way, which not only prevent training stagnation and lead
to a robust pretrained model in the pretraining stage, but also enhance the
model's target performance in the self-training stage. Experiments on three
challenging scenarios and a theoretical analysis verify the effectiveness of
MTM.
