# PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond

arXiv: https://arxiv.org/abs/2105.02184 · HTML: https://mlsanity.com/paper/2105.02184/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 65 of its 77 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 30 of 65)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Mask Scoring R-CNN](https://mlsanity.com/paper/1903.00241/) (2019)
- [RepPoints: Point Set Representation for Object Detection](https://mlsanity.com/paper/1904.11490/) (2019)
- [Libra R-CNN: Towards Balanced Learning for Object Detection](https://mlsanity.com/paper/1904.02701/) (2019)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [UnitBox: An Advanced Object Detection Network](https://mlsanity.com/paper/1608.01471/) (2016)
- [PolarMask: Single Shot Instance Segmentation with Polar Representation](https://mlsanity.com/paper/1909.13226/) (2019)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)

## Built on by (most cited first, 27 of 27)

- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [Advances in Deep Concealed Scene Understanding](https://mlsanity.com/paper/2304.11234/) (2023)
- [E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation](https://mlsanity.com/paper/2203.04074/) (2022)
- [Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution](https://mlsanity.com/paper/2212.01579/) (2022)
- [Vision Transformers Are Good Mask Auto-Labelers](https://mlsanity.com/paper/2301.03992/) (2023)
- [BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction](https://mlsanity.com/paper/2211.03373/) (2022)
- [FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation](https://mlsanity.com/paper/2111.02394/) (2021)
- [Recurrent Generic Contour-based Instance Segmentation with Progressive Learning](https://mlsanity.com/paper/2301.08898/) (2023)
- [Zoom Text Detector](https://mlsanity.com/paper/2209.03014/) (2022)
- [Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation](https://mlsanity.com/paper/2110.09734/) (2021)
- [AutoKary2022: A Large-Scale Densely Annotated Dataset for Chromosome Instance Segmentation](https://mlsanity.com/paper/2303.15839/) (2023)
- [Grouping Boundary Proposals for Fast Interactive Image Segmentation](https://mlsanity.com/paper/2309.04169/) (2023)
- [Learning from Exemplars for Interactive Image Segmentation](https://mlsanity.com/paper/2406.11472/) (2024)
- [ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles](https://mlsanity.com/paper/2204.04635/) (2022)
- [Automatic dataset generation for specific object detection](https://mlsanity.com/paper/2207.07867/) (2022)
- [CPR++: Object Localization via Single Coarse Point Supervision](https://mlsanity.com/paper/2401.17203/) (2024)
- [MP-PolarMask: A Faster and Finer Instance Segmentation for Concave Images](https://mlsanity.com/paper/2406.01356/) (2024)
- [ContourFormer: Real-Time Contour-Based End-to-End Instance Segmentation Transformer](https://mlsanity.com/paper/2501.17688/) (2025)
- [PolarRec: Radio Interferometric Data Reconstruction with Polar Coordinate Representation](https://mlsanity.com/paper/2308.14610/) (2023)
- [Edge Wasserstein Distance Loss for Oriented Object Detection](https://mlsanity.com/paper/2312.07048/) (2023)
- [Generalized Mask-aware IoU for Anchor Assignment for Real-time Instance Segmentation](https://mlsanity.com/paper/2312.17031/) (2023)
- [Skeleton-Guided Sparse Anchors for Rotated Instance Segmentation in Cell Microscopy](https://mlsanity.com/paper/2401.09895/) (2024)
- [AIWR: Aerial Image Water Resource Dataset for Segmentation Analysis](https://mlsanity.com/paper/2411.01797/) (2024)
- [Bridging Object Detection and Segmentation with Polygon Detection Transformers](https://mlsanity.com/paper/2603.09245/) (2026)
- [Geometry-Guided Self-Supervision for Ultra-Fine-Grained Recognition with Limited Data](https://mlsanity.com/paper/2604.19345/) (2026)
- [Geodesics with Unified Tangent-constrained Priors and Curvature Regularization](https://mlsanity.com/paper/2606.00139/) (2026)
- [Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation](https://mlsanity.com/paper/2606.14912/) (2026)

## Abstract (arXiv)

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.