  Fully supervised object detection requires training images in which all
instances are annotated. This is actually impractical due to the high labor and
time costs and the unavoidable missing annotations. As a result, the incomplete
annotation in each image could provide misleading supervision and harm the
training. Recent works on sparsely annotated object detection alleviate this
problem by generating pseudo labels for the missing annotations. Such a
mechanism is sensitive to the threshold of the pseudo label score. However, the
effective threshold is different in different training stages and among
different object detectors. Therefore, the current methods with fixed
thresholds have sub-optimal performance, and are difficult to be applied to
other detectors. In order to resolve this obstacle, we propose a Calibrated
Teacher, of which the confidence estimation of the prediction is well
calibrated to match its real precision. In this way, different detectors in
different training stages would share a similar distribution of the output
confidence, so that multiple detectors could share the same fixed threshold and
achieve better performance. Furthermore, we present a simple but effective
Focal IoU Weight (FIoU) for the classification loss. FIoU aims at reducing the
loss weight of false negative samples caused by the missing annotation, and
thus works as the complement of the teacher-student paradigm. Extensive
experiments show that our methods set new state-of-the-art under all different
sparse settings in COCO. Code will be available at
https://github.com/Whileherham/CalibratedTeacher.
