Panoptic Segmentation
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).
( Image credit: Detectron2 )
Leaderboards
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Greatest papers with code
UPSNet: A Unified Panoptic Segmentation Network
CVPR 2019
• uber-research/UPSNet
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More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification.
#2 best model for
Panoptic Segmentation
on Cityscapes val
(using extra training data)
AdaptIS: Adaptive Instance Selection Network
ICCV 2019
• saic-vul/adaptis
• 
Given an input image and a point $(x, y)$, it generates a mask for the object located at $(x, y)$.
Seamless Scene Segmentation
CVPR 2019
• mapillary/seamseg
•
In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results.
Panoptic Segmentation
We propose and study a task we name panoptic segmentation (PS).
#3 best model for
Panoptic Segmentation
on Cityscapes val
(using extra training data)
INSTANCE SEGMENTATION PANOPTIC SEGMENTATION SCENE PARSING SCENE SEGMENTATION
Weakly- and Semi-Supervised Panoptic Segmentation
We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks.
#12 best model for
Panoptic Segmentation
on Cityscapes val
INSTANCE SEGMENTATION PANOPTIC SEGMENTATION WEAKLY-SUPERVISED INSTANCE SEGMENTATION WEAKLY-SUPERVISED PANOPTIC SEGMENTATION WEAKLY-SUPERVISED SEMANTIC SEGMENTATION
Single Network Panoptic Segmentation for Street Scene Understanding
• DdeGeus/single-network-panoptic-segmentation
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Our network is evaluated on two street scene datasets: Cityscapes and Mapillary Vistas.
INSTANCE SEGMENTATION PANOPTIC SEGMENTATION SCENE UNDERSTANDING
Generator evaluator-selector net: a modular approach for panoptic segmentation
• sagieppel/Generator-evaluator-selector-net-a-modular-approach-for-panoptic-segmentation
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The generator/evaluator approach for this case consists of two independent convolutional neural nets: a generator net that suggests variety segments corresponding to objects and distinct regions in the image and an evaluator net that chooses the best segments to be merged into the segmentation map.