  We present a simple, fully-convolutional model for real-time instance
segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a
single Titan Xp, which is significantly faster than any previous competitive
approach. Moreover, we obtain this result after training on only one GPU. We
accomplish this by breaking instance segmentation into two parallel subtasks:
(1) generating a set of prototype masks and (2) predicting per-instance mask
coefficients. Then we produce instance masks by linearly combining the
prototypes with the mask coefficients. We find that because this process
doesn't depend on repooling, this approach produces very high-quality masks and
exhibits temporal stability for free. Furthermore, we analyze the emergent
behavior of our prototypes and show they learn to localize instances on their
own in a translation variant manner, despite being fully-convolutional.
Finally, we also propose Fast NMS, a drop-in 12 ms faster replacement for
standard NMS that only has a marginal performance penalty.
