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Updated: Nov 5, 2025

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy
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PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond.

Enze Xie, Wenhai Wang, Mingyu Ding

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 14, 2021
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    Summary
    This summary is machine-generated.

    PolarMask++ simplifies instance segmentation using polar coordinates, unifying object detection and segmentation. This anchor-box free, single-shot framework achieves state-of-the-art results in various applications.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Instance segmentation complexity hinders real-world applications.
    • Existing methods often involve intricate pipelines.

    Purpose of the Study:

    • To introduce PolarMask++, an efficient anchor-box free, single-shot instance segmentation framework.
    • To reformulate instance segmentation using polar coordinates for reduced complexity.

    Main Methods:

    • Developed PolarMask++, predicting object contours in polar coordinates.
    • Introduced soft polar centerness and polar IoU loss for optimized contour regression.
    • Integrated a Refined Feature Pyramid for enhanced multi-scale feature representation.

    Main Results:

    • PolarMask++ unifies instance segmentation and object detection, reducing design and computational complexity.
    • Achieved competitive results on the COCO dataset.
    • Established new state-of-the-art performance on text detection and cell segmentation datasets.

    Conclusions:

    • The polar representation offers a novel perspective for single-shot instance segmentation.
    • PolarMask++ demonstrates significant efficiency and accuracy improvements.
    • The framework's fully convolutional nature allows easy integration with existing detectors.