  Self-training for unsupervised domain adaptive object detection is a
challenging task, of which the performance depends heavily on the quality of
pseudo boxes. Despite the promising results, prior works have largely
overlooked the uncertainty of pseudo boxes during self-training. In this paper,
we present a simple yet effective framework, termed as Probabilistic Teacher
(PT), which aims to capture the uncertainty of unlabeled target data from a
gradually evolving teacher and guides the learning of a student in a mutually
beneficial manner. Specifically, we propose to leverage the uncertainty-guided
consistency training to promote classification adaptation and localization
adaptation, rather than filtering pseudo boxes via an elaborate confidence
threshold. In addition, we conduct anchor adaptation in parallel with
localization adaptation, since anchor can be regarded as a learnable parameter.
Together with this framework, we also present a novel Entropy Focal Loss (EFL)
to further facilitate the uncertainty-guided self-training. Equipped with EFL,
PT outperforms all previous baselines by a large margin and achieve new
state-of-the-arts.
