Panoptic Segmentation Edit Task
Computer Vision • Semantic Segmentation
About
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
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Libraries
Datasets
Greatest papers with code
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
ECCV 2020
• tensorflow/models
• 
We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.
OPTICAL FLOW ESTIMATION PANOPTIC SEGMENTATION PATCH MATCHING SCENE SEGMENTATION
Mask R-CNN
ICCV 2017
• tensorflow/models
•
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Ranked #1 on
Real-Time Object Detection
on COCO minival
(MAP metric)
3D INSTANCE SEGMENTATION HUMAN PART SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING MULTI-PERSON POSE ESTIMATION MULTI-TISSUE NUCLEUS SEGMENTATION NUCLEAR SEGMENTATION PANOPTIC SEGMENTATION REAL-TIME OBJECT DETECTION
Panoptic-DeepLab
• facebookresearch/detectron2
• 
The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e. g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression.
Panoptic Feature Pyramid Networks
CVPR 2019
• facebookresearch/detectron2
•
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.
Ranked #4 on
Panoptic Segmentation
on KITTI Panoptic Segmentation
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
arXiv preprint arXiv:2006.02334 2020
• open-mmlab/mmdetection
•
In this paper, we explore this mechanism in the backbone design for object detection.
Ranked #3 on
Panoptic Segmentation
on COCO test-dev
INSTANCE SEGMENTATION OBJECT DETECTION PANOPTIC SEGMENTATION
End-to-End Object Detection with Transformers
ECCV 2020
• open-mmlab/mmdetection
•
We present a new method that views object detection as a direct set prediction problem.
Ranked #3 on
Panoptic Segmentation
on COCO panoptic
ResNeSt: Split-Attention Networks
It is well known that featuremap attention and multi-path representation are important for visual recognition.
Ranked #2 on
Panoptic Segmentation
on COCO panoptic
IMAGE CLASSIFICATION INSTANCE SEGMENTATION OBJECT DETECTION PANOPTIC SEGMENTATION TRANSFER LEARNING
Feature Pyramid Encoding Network for Real-time Semantic Segmentation
• osmr/imgclsmob
• 
Although current deep learning methods have achieved impressive results for semantic segmentation, they incur high computational costs and have a huge number of parameters.
SOLOv2: Dynamic and Fast Instance Segmentation
NeurIPS 2020
• aim-uofa/adet
•
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.
Ranked #11 on
Instance Segmentation
on COCO test-dev
INSTANCE SEGMENTATION OBJECT DETECTION PANOPTIC SEGMENTATION
Hierarchical Multi-Scale Attention for Semantic Segmentation
• NVIDIA/semantic-segmentation
•
Multi-scale inference is commonly used to improve the results of semantic segmentation.
Ranked #1 on
Semantic Segmentation
on Cityscapes test
(using extra training data)