Panoptic Segmentation Article Swipe
Alexander Kirillov
,
Kaiming He
,
Ross Girshick
,
Carsten Rother
,
Piotr Dollár
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/cvpr.2019.00963
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/cvpr.2019.00963
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.
Related Topics
Concepts
Segmentation
Computer science
Artificial intelligence
Parsing
Metric (unit)
Task (project management)
Object (grammar)
Computer vision
Segmentation-based object categorization
Scale-space segmentation
Panopticon
Image segmentation
Class (philosophy)
Pattern recognition (psychology)
Political science
Politics
Law
Economics
Operations management
Management
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/cvpr.2019.00963
- OA Status
- green
- Cited By
- 77
- References
- 63
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2798040152
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2798040152Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/cvpr.2019.00963Digital Object Identifier
- Title
-
Panoptic SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-06-01Full publication date if available
- Authors
-
Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr DollárList of authors in order
- Landing page
-
https://doi.org/10.1109/cvpr.2019.00963Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1801.00868Direct OA link when available
- Concepts
-
Segmentation, Computer science, Artificial intelligence, Parsing, Metric (unit), Task (project management), Object (grammar), Computer vision, Segmentation-based object categorization, Scale-space segmentation, Panopticon, Image segmentation, Class (philosophy), Pattern recognition (psychology), Political science, Politics, Law, Economics, Operations management, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
77Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 11, 2023: 12, 2022: 12, 2021: 13, 2020: 11Per-year citation counts (last 5 years)
- References (count)
-
63Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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