  Most existing domain adaptive object detection methods exploit adversarial
feature alignment to adapt the model to a new domain. Recent advances in
adversarial feature alignment strives to reduce the negative effect of
alignment, or negative transfer, that occurs because the distribution of
features varies depending on the category of objects. However, by analyzing the
features of the anchor-free one-stage detector, in this paper, we find that
negative transfer may occur because the feature distribution varies depending
on the regression value for the offset to the bounding box as well as the
category. To obtain domain invariance by addressing this issue, we align the
feature conditioned on the offset value, considering the modality of the
feature distribution. With a very simple and effective conditioning method, we
propose OADA (Offset-Aware Domain Adaptive object detector) that achieves
state-of-the-art performances in various experimental settings. In addition, by
analyzing through singular value decomposition, we find that our model enhances
both discriminability and transferability.
