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.

本文提出了一种名为 Probabilistic Teacher（PT）的简单但有效的框架，通过从逐渐演变的 “老师” 中捕捉未标记目标数据的不确定性来引导学生的学习，从而优化了无监督域适应目标检测任务的性能并提出了 Entropy Focal Loss（EFL）以进一步加强不确定性引导的自训练方法，结果表明 PT 在多个基准测试中均超过以往工作的表现并取得了最新的最佳结果。