  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.
