Panoptic Segmentation
85 papers with code • 10 benchmarks • 14 datasets
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 )
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 implementationsDatasets
COCO
Cityscapes
KITTI
ScanNet
SemanticKITTI
BDD100K
Mapillary Vistas Dataset
Cityscapes Panoptic Parts
Pascal Panoptic Parts
PASTIS
Most implemented papers
Mask R-CNN
matterport/Mask_RCNN
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ICCV 2017
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
ResNeSt: Split-Attention Networks
It is well known that featuremap attention and multi-path representation are important for visual recognition.
End-to-End Object Detection with Transformers
facebookresearch/detr
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ECCV 2020
We present a new method that views object detection as a direct set prediction problem.
SOLOv2: Dynamic and Fast Instance Segmentation
WXinlong/SOLO
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NeurIPS 2020
Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.
Panoptic Feature Pyramid Networks
facebookresearch/detectron2
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CVPR 2019
In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks.
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.
PVTv2: Improved Baselines with Pyramid Vision Transformer
whai362/PVT
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We hope this work will facilitate state-of-the-art Transformer researches in computer vision.
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
bowenc0221/panoptic-deeplab
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CVPR 2020
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed.
