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。