  Recently, adversarial-based domain adaptive object detection (DAOD) methods
have been developed rapidly. However, there are two issues that need to be
resolved urgently. Firstly, numerous methods reduce the distributional shifts
only by aligning all the feature between the source and target domain, while
ignoring the private information of each domain. Secondly, DAOD should consider
the feature alignment on object existing regions in images. But redundancy of
the region proposals and background noise could reduce the domain
transferability. Therefore, we establish a Feature Separation and Alignment
Network (FSANet) which consists of a gray-scale feature separation (GSFS)
module, a local-global feature alignment (LGFA) module and a
region-instance-level alignment (RILA) module. The GSFS module decomposes the
distractive/shared information which is useless/useful for detection by a
dual-stream framework, to focus on intrinsic feature of objects and resolve the
first issue. Then, LGFA and RILA modules reduce the distributional shifts of
the multi-level features. Notably, scale-space filtering is exploited to
implement adaptive searching for regions to be aligned, and instance-level
features in each region are refined to reduce redundancy and noise mentioned in
the second issue. Various experiments on multiple benchmark datasets prove that
our FSANet achieves better performance on the target domain detection and
surpasses the state-of-the-art methods.
