In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher(Source code are available at: \url{https://github.com/HunterJ-Lin/ActiveTeacher}) for semi-supervised object detection (SSOD). Active Teacher extends the teacher-student framework to an iterative version, where the label set is partially initialized and gradually augmented by evaluating three key factors of unlabeled examples, including difficulty, information and diversity. With this design, Active Teacher can maximize the effect of limited label information while improving the quality of pseudo-labels. To validate our approach, we conduct extensive experiments on the MS-COCO benchmark and compare Active Teacher with a set of recently proposed SSOD methods. The experimental results not only validate the superior performance gain of Active Teacher over the compared methods, but also show that it enables the baseline network, ie, Faster-RCNN, to achieve 100% supervised performance with much less label expenditure, ie 40% labeled examples on MS-COCO. More importantly, we believe that the experimental analyses in this paper can provide useful empirical knowledge for data annotation in practical applications.

本文研究了教师-学生学习的数据初始化问题，并提出了一种称为Active Teacher的新算法，用于半监督目标检测。Active Teacher可以最大限度地利用有限的标签信息来提高半监督的性能。我们在MS-COCO基准测试上进行了大量实验，并将Active Teacher与一组最近提出的半监督目标检测方法进行了比较，结果表明Active Teacher具有较高的性能，并能够在大大减少标签预算的情况下实现完全监督性能，这对于实际应用的数据注释提供了有用的经验知识。

半监督对象检测的主动教师