Panoptic Segmentation
Open accessAbstract
We propose and study a task we name panoptic segmentation (PS). Panoptic segmentation unifies the typically distinct tasks of semantic segmentation (assign a class label to each pixel) and instance segmentation (detect and segment each object instance). The proposed task requires generating a coherent scene segmentation that is rich and complete, an important step toward real-world vision systems. While early work in computer vision addressed related image/scene parsing tasks, these are not currently popular, possibly due to lack of appropriate metrics or associated recognition challenges. To address this, we propose a novel panoptic quality (PQ) metric that captures performance for all classes (stuff and things) in an interpretable and unified manner. Using the proposed metric, we perform a rigorous study of both human and machine performance for PS on three existing datasets, revealing interesting insights about the task. The aim of our work is to revive the interest of the community in a more unified view of image segmentation. For more analysis and up-to-date results, please check the arXiv version of the paper: https://arxiv.org/abs/1801.00868.
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- Authors
- Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P.
- Affiliations
- Meta (United States) (US)
- Facility for Antiproton and Ion Research (DE)
- Heidelberg University (DE)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 9396-9405
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2798040152
- Cited by
- 1,659
- References
- 46
- Citations by year
- 2026: 113 · 2025: 240 · 2024: 333 · 2023: 277 · 2022: 231 · 2021: 236 · 2020: 148 · 2019: 64 · 2018: 16
- FWCI
- 54.72
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- panoptic segmentation, semantic segmentation, instance segmentation, scene parsing, human performance comparison, image segmentation evaluation
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