  Recent object detectors use four-coordinate bounding box (bbox) regression to
predict object locations. Providing additional information indicating the
object positions and coordinates will improve detection performance. Thus, we
propose two types of masks: a bbox mask and a bounding shape (bshape) mask, to
represent the object's bbox and boundary shape, respectively. For each of these
types, we consider two variants: the Thick model and the Scored model, both of
which have the same morphology but differ in ways to make their boundaries
thicker. To evaluate the proposed masks, we design extended frameworks by
adding a bshape mask (or a bbox mask) branch to a Faster R-CNN framework, and
call this BshapeNet (or BboxNet). Further, we propose BshapeNet+, a network
that combines a bshape mask branch with a Mask R-CNN to improve instance
segmentation as well as detection. Among our proposed models, BshapeNet+
demonstrates the best performance in both tasks and achieves highly competitive
results with state of the art (SOTA) models. Particularly, it improves the
detection results over Faster R-CNN+RoIAlign (37.3% and 28.9%) with a detection
AP of 42.4% and 32.3% on MS COCO test-dev and Cityscapes val, respectively.
Furthermore, for small objects, it achieves 24.9% AP on COCO test-dev, a
significant improvement over previous SOTA models. For instance segmentation,
it is substantially superior to Mask R-CNN on both test datasets.
