  We propose an approach for unsupervised adaptation of object detectors from
label-rich to label-poor domains which can significantly reduce annotation
costs associated with detection. Recently, approaches that align distributions
of source and target images using an adversarial loss have been proven
effective for adapting object classifiers. However, for object detection, fully
matching the entire distributions of source and target images to each other at
the global image level may fail, as domains could have distinct scene layouts
and different combinations of objects. On the other hand, strong matching of
local features such as texture and color makes sense, as it does not change
category level semantics. This motivates us to propose a novel method for
detector adaptation based on strong local alignment and weak global alignment.
Our key contribution is the weak alignment model, which focuses the adversarial
alignment loss on images that are globally similar and puts less emphasis on
aligning images that are globally dissimilar. Additionally, we design the
strong domain alignment model to only look at local receptive fields of the
feature map. We empirically verify the effectiveness of our method on four
datasets comprising both large and small domain shifts. Our code is available
at \url{https://github.com/VisionLearningGroup/DA_Detection}
