  In this paper, we propose a simple yet efficientinstance segmentation
approach based on the single-stage anchor-free detector, termed SAIS. In our
approach, the instancesegmentation task consists of two parallel subtasks which
re-spectively predict the mask coefficients and the mask prototypes.Then,
instance masks are generated by linearly combining theprototypes with the mask
coefficients. To enhance the quality ofinstance mask, the information from
regression and classificationis fused to predict the mask coefficients. In
addition, center-aware target is designed to preserve the center coordination
ofeach instance, which achieves a stable improvement in instancesegmentation.
The experiment on MS COCO shows that SAISachieves the performance of the
exiting state-of-the-art single-stage methods with a much less memory footpr
