  In this paper, we propose a single-shot instance segmentation method, which
is simple, fast and accurate. There are two main challenges for one-stage
instance segmentation: object instances differentiation and pixel-wise feature
alignment. Accordingly, we decompose the instance segmentation into two
parallel subtasks: Local Shape prediction that separates instances even in
overlapping conditions, and Global Saliency generation that segments the whole
image in a pixel-to-pixel manner. The outputs of the two branches are assembled
to form the final instance masks. To realize that, the local shape information
is adopted from the representation of object center points. Totally trained
from scratch and without any bells and whistles, the proposed CenterMask
achieves 34.5 mask AP with a speed of 12.3 fps, using a single-model with
single-scale training/testing on the challenging COCO dataset. The accuracy is
higher than all other one-stage instance segmentation methods except the 5
times slower TensorMask, which shows the effectiveness of CenterMask. Besides,
our method can be easily embedded to other one-stage object detectors such as
FCOS and performs well, showing the generalization of CenterMask.

Computer Vision and Pattern Recognition