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.
翻译:对不受监督的域域适应性物体探测进行自我培训是一项艰巨的任务,其性能在很大程度上取决于伪箱的质量。尽管取得了令人乐观的成果,但先前的工程在自我培训期间基本上忽略了伪箱的不确定性。在本文中,我们提出了一个简单而有效的框架,称为概率教师(PT),目的是从一个逐步演变的教师那里获取未贴标签的目标数据的不确定性,并以互利的方式指导学生的学习。具体地说,我们提议利用不确定性指导的一致性培训,促进分类适应和本地化适应,而不是通过精心设定的信任门槛过滤假箱。此外,我们与本地化适应同时进行锚定适应,因为锚可被视为一种可学习的参数。我们与这个框架一起,还提出了一个新的英特罗比协调损失(EFL),以进一步便利不确定性引导的自我培训。我们用EFL,PT超越了所有以前的基线,大幅度,并实现了新的状态。