Review : PolarMask: Single Shot Instance Segmentation with Polar Representation Paper link : https://arxiv.org/abs/1909.13226 (CVPR2020 accepted)

1. PolarMask: Single Shot Instance Segmentation
with Polar Representation
The University of Hong Kong, Sensetime
Yonsei University Severance Hospital CCIDS
Choi Dongmin

2. Abstract
• PolarMask
- single shot instance segmentation
- anchor-box free
- prediction contour of instance through instance center
classification and dense distance regression in a polar coordinate
- 32.9 % mAP on the challenge COCO dataset (single-scale test)

3. Introduction
• Instance Segmentation
- challenging
- Mask R-CNN (two-stage)
• Goal
- a conceptually simple mask
prediction module easily plugged
into many detectors, enabling
instance segmentation
https://medium.com/analytics-vidhya/instance-segmentation-using-mask-r-cnn-on-a-custom-dataset-78631845de2a

4. • Mask representation
- (b) binary (BG vs FG)
: hard for single-shot method
- (c) Cartesian coordinates of the point
composing the contour
- (d) the angle and the distance as the
coordinate to locate points
• Polar representation
(1) The origin point = the center of object
(2) The point in contour is determined by
the distance and angle
(3) The angle is naturally directional and
makes it very convenient to connect the
points into a whole contour
Introduction

5. Introduction
• Mask representation
- (b) binary (BG vs FG)
: hard for single-shot method
- (c) Cartesian coordinates of the point
composing the contour
- (d) the angle and the distance as the
coordinate to locate points
• Polar representation
(1) The origin point = the center of object
(2) The point in contour is determined by
the distance and angle
(3) The angle is naturally directional and
makes it very convenient to connect the
points into a whole contour

6. Related Work
Two-Stage Instance Segmentation
Detect and Segment
K He et al. Mask R-CNN. ICCV 2017

7. Related Work
One Stage Instance Segmentation
D Bolya et al. YOLACT: Real-time Instance Segmentation. ICCV 2019
Prototype masks Bounding Box
Mask coefficients

8. Related Work
Polar Representation
U Schmidt et al. Cell Detection with Star-convex Polygons. MICCAI 2018

9. Related Work
FCOS
Z Tian et al. FCOS: Fully Convolutional One-Stage Object Detection. ICCV 2019

10. Our Method
PolarMask Architecture

11. Our Method
Polar Mask Segmentation
1. Polar Representation

12. Our Method
Polar Mask Segmentation
1. Polar Representation
points on the contour (xi, yi), i = 1, 2, …, N
center (xc, yc)
angle interval Δθ
raysn
Instance Segmentation
= Instance center classification & Dense distance regression
xi = cos θi × di + xc
yi = sin θi × di + yc

13. Our Method
Polar Mask Segmentation
2. Polar Centerness
Z Tian et al. FCOS: Fully Convolutional One-Stage Object Detection. ICCV 2019
Centerness in FCOS

14. Our Method
Polar Mask Segmentation
2. Polar Centerness
Z Tian et al. FCOS: Fully Convolutional One-Stage Object Detection. ICCV 2019
FCOS PolarMask

15. Our Method
Polar Mask Segmentation
2. Polar IoU Loss
The power form ( ) has little impact
and
d2
Δθ =
2π
N

16. Our Method
Polar Mask Segmentation
2. Polar IoU Loss
- Advantages
(1) differentiable and easy to implement
parallel computations (fast training)
(2) predicts the regression targets
as a whole
(3) automatically balance classification
loss and regression loss

17. Experiments
Dataset : Challenging COCO dataset
- Training dataset : the union of 80K train + 35K val images
- Test dataset : remaining 5K val images & test-dev dataset
- Singe scale training and testing
- Short-edge as 800

18. Experiments
Training details
- Backbone : ResNet-50-FPN (ImageNet pre-trained)
- Optimization : SGD / 90K iterations / 16 batch size
Weight decay = 0.0001 / Momentum = 0.9
LR = 0.01 (decay at 60K and 80K)

19. Experiments
Training details

20. Experiments
Number of Rays

21. Experiments
Polar IoU Loss vs. Smooth-L1 Loss

22. Experiments
Polar IoU Loss vs. Smooth-L1 Loss

23. Experiments
Polar Centerness vs. Centerness

24. Experiments
Box Branch
Test whether the additional bounding box branch can help improve the mask AP

25. Experiments
Backbone Architecture

26. Experiments
Accuracy/speed trade-off on ResNet-50

27. Experiments
Comparison against SOTA on COCO test-dev

28. Experiments
Comparison against SOTA on COCO test-dev

29. Conclusion
• PolarMask
- a single shot anchor-box free instance segmentation method
- represent a mask by its contour
- simple and clean as single-shot object detectors

30. Thank you