Papers › Panoptic Segmentation

Panoptic Segmentation

3 Jan 2018CVPR 2019 6arXiv:1801.00868archive 2025-07-28

Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollár

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.

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Code

cocodataset/panopticapi officialmentioned on GitHub report
ChristophReich1996/TYC-Dataset mentioned on GitHubpytorch report
banus/umf_unet mentioned on GitHubpytorch report
dhassault/panoptic_segmentation mentioned on GitHubmxnet report
jlazarow/learning_instance_occlusion mentioned on GitHubpytorch report
kdethoor/panoptictorch mentioned on GitHubpytorch report
looooongchen/sortedap mentioned on GitHub report

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Tasks

Image SegmentationInstance SegmentationPanoptic SegmentationScene ParsingScene SegmentationSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Panoptic Segmentation Cityscapes val MRCNN + PSPNet (ResNet-101) AP 36.4 #24 of 37 Archive leaderboard report
Panoptic Segmentation Cityscapes val MRCNN + PSPNet (ResNet-101) PQ 61.2 #24 of 37 Archive leaderboard report
Panoptic Segmentation Cityscapes val MRCNN + PSPNet (ResNet-101) PQst 66.4 #24 of 37 Archive leaderboard report
Panoptic Segmentation Cityscapes val MRCNN + PSPNet (ResNet-101) PQth 54 #24 of 37 Archive leaderboard report

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Methods

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionGlobal Average PoolingKaiming InitializationMax PoolingReLUResidual BlockResidual Connection

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