Panoptic Segmentation
213 papers with code • 24 benchmarks • 32 datasets
Panoptic Segmentation is a computer vision task that combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. The goal of panoptic segmentation is to segment the image into semantically meaningful parts or regions, while also detecting and distinguishing individual instances of objects within those regions. In a given image, every pixel is assigned a semantic label, and pixels belonging to "things" classes (countable objects with instances, like cars and people) are assigned unique instance IDs. ( Image credit: Detectron2 )
Benchmarks
These leaderboards are used to track progress in Panoptic Segmentation
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Libraries
Use these libraries to find Panoptic Segmentation models and implementations
MS COCO
Cityscapes
KITTI
ScanNet
ADE20K
NYUv2
SemanticKITTI
SUN RGB-D
S3DIS
BDD100K
Most implemented papers
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints.
CenterMask : Real-Time Anchor-Free Instance Segmentation
youngwanLEE/CenterMask
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arXiv 2019
We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively.
Hierarchical Multi-Scale Attention for Semantic Segmentation
NVIDIA/semantic-segmentation
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Multi-scale inference is commonly used to improve the results of semantic segmentation.
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
joe-siyuan-qiao/DetectoRS
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CVPR 2021
In this paper, we explore this mechanism in the backbone design for object detection.
Fully Convolutional Networks for Panoptic Segmentation
yanwei-li/PanopticFCN
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CVPR 2021
In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN.
Masked-attention Mask Transformer for Universal Image Segmentation
facebookresearch/Mask2Former
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CVPR 2022
While only the semantics of each task differ, current research focuses on designing specialized architectures for each task.
Focal Modulation Networks
For semantic segmentation with UPerNet, FocalNet base at single-scale outperforms Swin by 2. 4, and beats Swin at multi-scale (50. 5 v. s.
Seamless Scene Segmentation
mapillary/seamseg
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CVPR 2019
In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results.
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
google-research/deeplab2
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ECCV 2020
In this paper, we attempt to remove this constraint by factorizing 2D self-attention into two 1D self-attentions.
Mask2Former for Video Instance Segmentation
facebookresearch/Mask2Former
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We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
