  Most state-of-the-art methods of object detection suffer from poor
generalization ability when the training and test data are from different
domains, e.g., with different styles. To address this problem, previous methods
mainly use holistic representations to align feature-level and pixel-level
distributions of different domains, which may neglect the instance-level
characteristics of objects in images. Besides, when transferring detection
ability across different domains, it is important to obtain the instance-level
features that are domain-invariant, instead of the styles that are
domain-specific. Therefore, in order to extract instance-invariant features, we
should disentangle the domain-invariant features from the domain-specific
features. To this end, a progressive disentangled framework is first proposed
to solve domain adaptive object detection. Particularly, base on disentangled
learning used for feature decomposition, we devise two disentangled layers to
decompose domain-invariant and domain-specific features. And the
instance-invariant features are extracted based on the domain-invariant
features. Finally, to enhance the disentanglement, a three-stage training
mechanism including multiple loss functions is devised to optimize our model.
In the experiment, we verify the effectiveness of our method on three
domain-shift scenes. Our method is separately 2.3\%, 3.6\%, and 4.0\% higher
than the baseline method \cite{saito2019strong}.
