  Deep learning-based object detectors have shown remarkable improvements.
However, supervised learning-based methods perform poorly when the train data
and the test data have different distributions. To address the issue, domain
adaptation transfers knowledge from the label-sufficient domain (source domain)
to the label-scarce domain (target domain). Self-training is one of the
powerful ways to achieve domain adaptation since it helps class-wise domain
adaptation. Unfortunately, a naive approach that utilizes pseudo-labels as
ground-truth degenerates the performance due to incorrect pseudo-labels. In
this paper, we introduce a weak self-training (WST) method and adversarial
background score regularization (BSR) for domain adaptive one-stage object
detection. WST diminishes the adverse effects of inaccurate pseudo-labels to
stabilize the learning procedure. BSR helps the network extract discriminative
features for target backgrounds to reduce the domain shift. Two components are
complementary to each other as BSR enhances discrimination between foregrounds
and backgrounds, whereas WST strengthen class-wise discrimination. Experimental
results show that our approach effectively improves the performance of the
one-stage object detection in unsupervised domain adaptation setting.
