PolarMask: Single Shot Instance Segmentation with Polar Representation (1909.13226v4)

Abstract: In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used as a mask prediction module for instance segmentation, by easily embedding it into most off-the-shelf detection methods. Our method, termed PolarMask, formulates the instance segmentation problem as instance center classification and dense distance regression in a polar coordinate. Moreover, we propose two effective approaches to deal with sampling high-quality center examples and optimization for dense distance regression, respectively, which can significantly improve the performance and simplify the training process. Without any bells and whistles, PolarMask achieves 32.9% in mask mAP with single-model and single-scale training/testing on challenging COCO dataset. For the first time, we demonstrate a much simpler and flexible instance segmentation framework achieving competitive accuracy. We hope that the proposed PolarMask framework can serve as a fundamental and strong baseline for single shot instance segmentation tasks. Code is available at: github.com/xieenze/PolarMask.

• The paper introduces PolarMask, a novel framework that leverages polar coordinates for single-shot instance segmentation.
• It simplifies segmentation by predicting object contours directly using center classification and dense distance regression.
• Experimental results demonstrate competitive performance with a 32.9% mask mAP on COCO and minimal computational overhead.

Insights into "PolarMask: Single Shot Instance Segmentation with Polar Representation"

The paper "PolarMask: Single Shot Instance Segmentation with Polar Representation" introduces a novel framework for instance segmentation, utilizing a polar coordinate system. This approach, termed PolarMask, reformulates instance segmentation tasks by focusing on predicting the contour of an instance through a combination of instance center classification and dense distance regression.

Key Contributions

PolarMask departs from traditional segmentation methods that generally rely on bounding box detection followed by pixel-wise classification within those boxes. Instead, PolarMask models the instance as a single center from which rays are emitted, reaching the contour of the object. The paper posits several advantages of this approach:

1. Simplicity and Efficiency: By employing a fully convolutional, anchor-box-free design, PolarMask simplifies the instance segmentation process. It can be seamlessly integrated into existing detection frameworks, introducing minimal computational overhead.
2. Competitive Performance: On the COCO dataset, PolarMask achieves a mask mean Average Precision (mAP) of 32.9% with single-model, single-scale training/testing. This performance is comparable to more complex instance segmentation methods that involve multi-stage processing or extensive data augmentation.
3. Generalization and Scalability: The paper highlights the potential of PolarMask to serve as a baseline for single-shot instance segmentation tasks. Its framework could be generalized to support different object detectors like FCOS and YOLO, owing to its simple modular design.

Technical Innovations

The authors propose several technical innovations to enhance the effectiveness of the Polar Representation:

• Polar Centerness: This concept is introduced to weigh the quality of center samples, which aids in sampling high-quality center points for effective instance mask prediction.
• Polar IoU Loss: A novel loss function tailored to optimize dense distance regression, facilitating better convergence and improved mask precision as compared to traditional smooth-$l_1$ losses.

The development of these components underscores the importance of treating the contour prediction task holistically, considering the correlations between distances to various ray endpoints.

Experimental Analysis

The authors provide a comprehensive empirical analysis to validate the efficacy of PolarMask:

• Number of Rays: They demonstrate the impact of varying the number of rays on upper bound precision and practical mAP, with results indicating that performance gains are achieved with an optimal number of 36 rays.
• Loss Function Evaluation: Comparisons between Polar IoU Loss and smooth-$l_1$ loss reveal the superiority of the former in achieving higher mask precision, due to its holistic approach to distance regression.
• Backbone Flexibility: PolarMask can leverage different architectural backbones like ResNet and ResNeXt, as well as incorporate advanced techniques such as deformable convolutions to bolster performance.

Implications and Future Directions

The PolarMask framework provides a compelling alternative to more complex methodologies for instance segmentation, emphasizing computational efficiency and streamlined design without sacrificing accuracy. The novel integration of polar coordinates in instance modeling opens avenues for further exploration in various AI applications. Future research may benefit from exploring enhanced training strategies, advanced backbone networks, or adaptations of PolarMask for real-time applications. Additionally, the impact of polar representation in 3D segmentation or video segmentation remains an intriguing prospect for further investigation.