  Tremendous efforts have been made to improve mask localization accuracy in
instance segmentation. Modern instance segmentation methods relying on fully
convolutional networks perform pixel-wise classification, which ignores object
boundaries and shapes, leading coarse and indistinct mask prediction results
and imprecise localization. To remedy these problems, we propose a conceptually
simple yet effective Boundary-preserving Mask R-CNN (BMask R-CNN) to leverage
object boundary information to improve mask localization accuracy. BMask R-CNN
contains a boundary-preserving mask head in which object boundary and mask are
mutually learned via feature fusion blocks. As a result, the predicted masks
are better aligned with object boundaries. Without bells and whistles, BMask
R-CNN outperforms Mask R-CNN by a considerable margin on the COCO dataset; in
the Cityscapes dataset, there are more accurate boundary groundtruths
available, so that BMask R-CNN obtains remarkable improvements over Mask R-CNN.
Besides, it is not surprising to observe that BMask R-CNN obtains more obvious
improvement when the evaluation criterion requires better localization (e.g.,
AP$_{75}$) as shown in Fig.1. Code and models are available at
\url{https://github.com/hustvl/BMaskR-CNN}.
