  Cross-domain object detection is challenging, because object detection model
is often vulnerable to data variance, especially to the considerable domain
shift between two distinctive domains. In this paper, we propose a new Unbiased
Mean Teacher (UMT) model for cross-domain object detection. We reveal that
there often exists a considerable model bias for the simple mean teacher (MT)
model in cross-domain scenarios, and eliminate the model bias with several
simple yet highly effective strategies. In particular, for the teacher model,
we propose a cross-domain distillation method for MT to maximally exploit the
expertise of the teacher model. Moreover, for the student model, we alleviate
its bias by augmenting training samples with pixel-level adaptation. Finally,
for the teaching process, we employ an out-of-distribution estimation strategy
to select samples that most fit the current model to further enhance the
cross-domain distillation process. By tackling the model bias issue with these
strategies, our UMT model achieves mAPs of 44.1%, 58.1%, 41.7%, and 43.1% on
benchmark datasets Clipart1k, Watercolor2k, Foggy Cityscapes, and Cityscapes,
respectively, which outperforms the existing state-of-the-art results in
notable margins. Our implementation is available at
https://github.com/kinredon/umt.
