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README.md

Awesome-Panoptic-Segmentation

This repo is a collection of the challenging panoptic segmentation, including papers, codes, and benchmark results, etc.

Panoptic Segmentation

Summarize in one sentence : Panoptic Segmentation proposes to solve the semantic segmentation(*Stuff*) and instance segmentation(*Thing*) in a unified and general manner.

Datasets

Generally, the datasets which contains both semantic and instance annotations can be used to solve the challenging panoptic task.

Benchmark Results

  • PC are the standard metrics described in DeeperLab.
  • COCO Benchmark
Method Backbone PQ PQ-Thing PQ-Stuff SQ RQ mIoU AP-Mask PC e2e
AUNet ResNet-101 45.2 54.4 31.3 80.6 54.7 - - - ✔️
UPSNet ResNet-101 42.5 48.6 33.4 - - 54.3 34.3 - ✔️
OANet ResNet-101 41.3 50.4 27.7 - - - - - ✔️
Panoptic FPN ResNet-101 40.9 48.3 29.7 - - - - - ✔️
DeeperLab Xception-71 34.3 37.5 29.6 77.1 43.1 - - 56.8 ✔️
  • Cityscapes Benchmark
Method Backbone PQ PQ-Thing PQ-Stuff SQ RQ mIoU AP-Mask PC e2e
Panoptic(Merge) - 61.2 66.4 54.0 80.9 74.4 - - -
Seamless ResNet-50 59.8 53.4 64.5 - - 75.4 31.9 - ✔️
UPSNet ResNet-50 59.3 54.6 62.7 79.7 73.0 75.2 33.3 - ✔️
TASCNet ResNet-101 59.2 56 61.5 - - 77.8 37.6 - ✔️
Panoptic FPN ResNet-101 58.1 52.0 62.5 - - 75.7 33.0 - ✔️
DeeperLab Xception-71 56.5 - - - - - - 75.6 ✔️
AUNet ResNet-101 59.0 54.8 62.1 - - 75.6 34.4 - ✔️
  • Mapillary Benchmark
Method Backbone PQ PQ-Thing PQ-Stuff SQ RQ mIoU AP-Mask PC e2e
Panoptic(Merge) - 38.3 41.8 35.7 73.6 47.7 - - -
Seamless ResNet-50 37.2 33.2 42.5 - - 50.2 16.3 - ✔️
TASCNet ResNet-101 32.6 31.3 34.4 - - 35.0 18.5 - ✔️
DeeperLab Xception-71 31.6 25.0 40.3 75.5 40.1 - - 55.3 ✔️

Papers

CVPR2019

  • Panoptic Segmentation: Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollár.
    "Panoptic Segmentation." CVPR (2019). [paper]

  • Panoptic FPN: Alexander Kirillov, Ross Girshick, Kaiming He, Piotr Dollár.
    "Panoptic Feature Pyramid Networks." CVPR (2019 oral). [paper] [unofficial code]

  • AUNet: Yanwei Li, Xinze Chen, Zheng Zhu, Lingxi Xie, Guan Huang, Dalong Du, Xingang Wang.
    "Attention-guided Unified Network for Panoptic Segmentation." CVPR (2019). [paper]

  • UPSNet: Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun.
    "UPSNet: A Unified Panoptic Segmentation Network." CVPR (2019 oral). [paper] [code]

  • DeeperLab: Tien-Ju Yang, Maxwell D. Collins, Yukun Zhu, Jyh-Jing Hwang, Ting Liu, Xiao Zhang, Vivienne Sze, George Papandreou, Liang-Chieh Chen.
    "DeeperLab: Single-Shot Image Parser." CVPR (2019). [paper] [project] [code]

  • TASCNet: Jie Li, Allan Raventos, Arjun Bhargava, Takaaki Tagawa, Adrien Gaidon.
    "Learning to Fuse Things and Stuff." CVPR (2019). [paper]

  • OANet: Huanyu Liu, Chao Peng, Changqian Yu, Jingbo Wang, Xu Liu, Gang Yu, Wei Jiang.
    "An End-to-End Network for Panoptic Segmentation." CVPR (2019). [paper]

  • Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari .
    "Interactive Full Image Segmentation by Considering All Regions Jointly." CVPR (2019). [paper]

ECCV2018

  • Qizhu Li, Anurag Arnab, Philip H.S. Torr.
    "Weakly- and Semi-Supervised Panoptic Segmentation." ECCV (2018). [paper] [code]

ArXiv

  • Seamless: Lorenzo Porzi, Samuel Rota Bulo, Aleksander Colovic, Peter Kontschieder.
    "Seamless Scene Segmentation." arXiv (2019). [paper]

  • Daan de Geus, Panagiotis Meletis, Gijs Dubbelman.
    "Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network." arXiv (2018). [paper]

  • Daan de Geus, Panagiotis Meletis, Gijs Dubbelman.
    "Single Network Panoptic Segmentation for Street Scene Understanding." arXiv (2019). [paper]

  • David Owen, Ping-Lin Chang.
    "Detecting Reflections by Combining Semantic and Instance Segmentation." arXiv (2019). [paper]

  • Gaku Narita, Takashi Seno, Tomoya Ishikawa, Yohsuke Kaji.
    "PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things." arXiv (2019). [paper]

Blogs

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