  To date, instance segmentation is dominated by twostage methods, as pioneered
by Mask R-CNN. In contrast, one-stage alternatives cannot compete with Mask
R-CNN in mask AP, mainly due to the difficulty of compactly representing masks,
making the design of one-stage methods very challenging. In this work, we
propose a simple singleshot instance segmentation framework, termed mask
encoding based instance segmentation (MEInst). Instead of predicting the
two-dimensional mask directly, MEInst distills it into a compact and
fixed-dimensional representation vector, which allows the instance segmentation
task to be incorporated into one-stage bounding-box detectors and results in a
simple yet efficient instance segmentation framework. The proposed one-stage
MEInst achieves 36.4% in mask AP with single-model (ResNeXt-101-FPN backbone)
and single-scale testing on the MS-COCO benchmark. We show that the much
simpler and flexible one-stage instance segmentation method, can also achieve
competitive performance. This framework can be easily adapted for other
instance-level recognition tasks. Code is available at:
https://git.io/AdelaiDet
