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.

提出了一种实时实例分割的全卷积模型，通过生成原型掩模和预测实例掩模系数两个并行子任务，使用线性组合的方法得到实例掩模。通过使用改进的锚点尺度和纵横比进行优化，同时引入新的快速掩模回归分支以及快速 NMS 算法，使得该模型在实时情况下能够接近于最先进的方法。

YOLACT++：更好的实时实例分割

YOLACT++: Better Real-time Instance Segmentation

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.

本研究提出了一种利用生成一组原型掩码和预测每个实例掩码系数的并行子任务的方法，进而实现在一台 Titan Xp 上每秒 33.5 帧且仅在一台 GPU 上进行训练即可在 MS COCO 上达到 29.8 mAP 的实时实例分割的全卷积模型，并且进一步通过分析原型的紧急行为和提出一个只有 12 ms 更快的可替换的 Fast NMS 来优化模型。