  The learning of the region proposal in object detection using the deep neural
networks (DNN) is divided into two tasks: binary classification and bounding
box regression task. However, traditional RPN (Region Proposal Network) defines
these two tasks as different problems, and they are trained independently. In
this paper, we propose a new region proposal learning method that considers the
bounding box offset's uncertainty in the objectness score. Our method redefines
RPN to a problem of minimizing the KL-divergence, difference between the two
probability distributions. We applied KL-RPN, which performs region proposal
using KL-Divergence, to the existing two-stage object detection framework and
showed that it can improve the performance of the existing method. Experiments
show that it achieves 2.6% and 2.0% AP improvements on MS COCO test-dev in
Faster R-CNN with VGG-16 and R-FCN with ResNet-101 backbone, respectively.
