  This paper focuses on a novel and challenging detection scenario: A majority
of true objects/instances is unlabeled in the datasets, so these
missing-labeled areas will be regarded as the background during training.
Previous art on this problem has proposed to use soft sampling to re-weight the
gradients of RoIs based on the overlaps with positive instances, while their
method is mainly based on the two-stage detector (i.e. Faster RCNN) which is
more robust and friendly for the missing label scenario. In this paper, we
introduce a superior solution called Background Recalibration Loss (BRL) that
can automatically re-calibrate the loss signals according to the pre-defined
IoU threshold and input image. Our design is built on the one-stage detector
which is faster and lighter. Inspired by the Focal Loss formulation, we make
several significant modifications to fit on the missing-annotation
circumstance. We conduct extensive experiments on the curated PASCAL VOC and MS
COCO datasets. The results demonstrate that our proposed method outperforms the
baseline and other state-of-the-arts by a large margin. Code available:
https://github.com/Dwrety/mmdetection-selective-iou.
