  In this paper, we introduce an anchor-free and single-shot instance
segmentation method, which is conceptually simple with 3 independent branches,
fully convolutional and can be used by easily embedding it into mobile and
embedded devices.
  Our method, refer as EOLO, reformulates the instance segmentation problem as
predicting semantic segmentation and distinguishing overlapping objects
problem, through instance center classification and 4D distance regression on
each pixel. Moreover, we propose one effective loss function to deal with
sampling a high-quality center of gravity examples and optimization for 4D
distance regression, which can significantly improve the mAP performance.
Without any bells and whistles, EOLO achieves 27.7$\%$ in mask mAP under IoU50
and reaches 30 FPS on 1080Ti GPU, with a single-model and single-scale
training/testing on the challenging COCO2017 dataset.
  For the first time, we show the different comprehension of instance
segmentation in recent methods, in terms of both up-bottom, down-up, and
direct-predict paradigms. Then we illustrate our model and present related
experiments and results. We hope that the proposed EOLO framework can serve as
a fundamental baseline for a single-shot instance segmentation task in
Real-time Industrial Scenarios.
