  We present a simple, fully-convolutional model for real-time (>30 fps)
instance segmentation that achieves competitive results on MS COCO evaluated on
a single Titan Xp, which is significantly faster than any previous
state-of-the-art 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. We also propose Fast NMS, a drop-in 12 ms faster
replacement for standard NMS that only has a marginal performance penalty.
Finally, by incorporating deformable convolutions into the backbone network,
optimizing the prediction head with better anchor scales and aspect ratios, and
adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1
mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art
approaches while still running at real-time.
