  Instance segmentation is a promising yet challenging topic in computer
vision. Recent approaches such as Mask R-CNN typically divide this problem into
two parts -- a detection component and a mask generation branch, and mostly
focus on the improvement of the detection part. In this paper, we present an
approach that extends Mask R-CNN with five novel optimization techniques for
improving the mask generation branch and reducing the conflicts between the
mask branch and the detection component in training. These five techniques are
independent to each other and can be flexibly utilized in building various
instance segmentation architectures for increasing the overall accuracy. We
demonstrate the effectiveness of our approach with tests on the COCO dataset.
