  Reducing the complexity of the pipeline of instance segmentation is crucial
for real-world applications. This work addresses this issue by introducing an
anchor-box free and single-shot instance segmentation framework, termed
PolarMask, which reformulates the instance segmentation problem as predicting
the contours of objects in the polar coordinate, with several appealing
benefits. (1) The polar representation unifies instance segmentation (masks)
and object detection (bounding boxes) into a single framework, reducing the
design and computational complexity. (2) Two modules are carefully designed
(i.e. soft polar centerness and polar IoU loss) to sample high-quality center
examples and optimize polar contour regression, making the performance of
PolarMask does not depend on the bounding box prediction results and thus
becomes more efficient in training. (3) PolarMask is fully convolutional and
can be easily embedded into most off-the-shelf detection methods. To further
improve the accuracy of the framework, a Refined Feature Pyramid is introduced
to further improve the feature representation at different scales, termed
PolarMask++. Extensive experiments demonstrate the effectiveness of both
PolarMask and PolarMask++, which achieve competitive results on instance
segmentation in the challenging COCO dataset with single-model and single-scale
training and testing, as well as new state-of-the-art results on rotate text
detection and cell segmentation. We hope the proposed polar representation can
provide a new perspective for designing algorithms to solve single-shot
instance segmentation. The codes and models are available at:
github.com/xieenze/PolarMask.
