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We propose and study a task we name panoptic segmentation (PS).
Information retrieval
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R. Vaillant, C. Monrocq, and Y. LeCun · 1994
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On seeing stuff: the perception of materials by humans and machines
E. H. Adelson · 2001
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Rapid object detection using a boosted cascade of simple features
P. Viola and M. Jones · 2001
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Introduction to graph theory
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Learning to detect natural image boundaries using local brightness, color, and texture cues
D. R. Martin, C. C. Fowlkes, and J. Malik · 2004
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Image parsing: Unifying segmentation, detection, and recognition
Z. Tu, X. Chen, A. L. Yuille, and S.-C. Zhu · 2005
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Textonboost: Joint appearance, shape and context modeling for multi-class object recog. and segm
J. Shotton, J. Winn, C. Rother, and A. Criminisi · 2006
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Discriminative models for multi-class object layout
C. Desai, D. Ramanan, and C. C. Fowlkes · 2011
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SIFT flow: Dense correspondence across scenes and its applications
C. Liu, J. Yuen, and A. Torralba · 2011
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Pedestrian detection: An evaluation of the state of the art
P. Dollár, C. Wojek, B. Schiele, and P. Perona · 2012
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Layered object models for image segmentation
Y. Yang, S. Hallman, D. Ramanan, and C. C. Fowlkes · 2012
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Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation
J. Yao, S. Fidler, and R. Urtasun · 2012
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Finding things: Image parsing with regions and per-exemplar detectors
J. Tighe and S. Lazebnik · 2013
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Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Relating things and stuff via object property interactions
M. Sun, B. Kim, P. Kohli, and S. Savarese · 2014
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Scene parsing with object instances and occlusion ordering
J. Tighe, M. Niethammer, and S. Lazebnik · 2014
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The PASCAL visual object classes challenge: A retrospective
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2015
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What makes for effective detection proposals?
J. Hosang, R. Benenson, P. Dollár, and B. Schiele · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollár · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Fully convolutional instance-aware semantic segmentation
Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei · 2017
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SGN: Sequential grouping networks for instance segmentation
S. Liu, J. Jia, S. Fidler, and R. Urtasun · 2017
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LSUN’17: insatnce segmentation task, UCenter winner team
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia · 2017
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Places challenge 2017: instance segmentation, Megvii (Face++) team
R. Luo, B. Jiang, T. Xiao, C. Peng, Y. Jiang, Z. Li, X. Zhang, G. Yu, Y. Mu, and J. Sun · 2017
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The mapillary vistas dataset for semantic understanding of street scenes
G. Neuhold, T. Ollmann, S. Rota Bulò, and P. Kontschieder · 2017
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LSUN’17: semantic segmentation task, PSPNet winner team
Y. Zhang, H. Zhao, and J. Shi · 2017
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Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Cited alongside, same era.
The three R’s of computer vision: Recognition, reconstruction and reorganization
J. Malik, P. Arbeláez, J. Carreira, K. Fragkiadaki, R. Girshick, G. Gkioxari, S. Gupta, B. Hariharan, A. Kar, and S. Tulsiani · 2016
Cited alongside, same era.
Cross-stitch networks for multi-task learning
I. Misra, A. Shrivastava, A. Gupta, and M. Hebert · 2016
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
Cited alongside, same era.
Pixelwise instance segmentation with a dynamically instantiated network
A. Arnab and P. H. Torr · 2017
Cited alongside, same era.
Deep watershed transform for instance segmentation
M. Bai and R. Urtasun · 2017
Cited alongside, same era.
Later among the works it cites.
Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
Later among the works it cites.
Scene parsing through ADE20K dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
Later among the works it cites.
Semantic amodal segmentation
Y. Zhu, Y. Tian, D. Mexatas, and P. Dollár · 2017
Later among the works it cites.
COCO-Stuff: Thing and stuff classes in context
H. Caesar, J. Uijlings, and V. Ferrari · 2018
Closest in time.
DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
Closest in time.
Learning to fuse things and stuff
J. Li, A. Raventos, A. Bhargava, T. Tagawa, and A. Gaidon · 2018
Closest in time.
Weakly-and semi-supervised panoptic segmentation
Q. Li, A. Arnab, and P. H. Torr · 2018
Closest in time.
Attention-guided unified network for panoptic segmentation
Y. Li, X. Chen, Z. Zhu, L. Xie, G. Huang, D. Du, and X. Wang · 2018
Closest in time.
Panoptic feature pyramid networks
A. Kirillov, R. Girshick, K. He, and P. Dollár · 2019
Closest in time.
An end-to-end network for panoptic segmentation
H. Liu, C. Peng, C. Yu, J. Wang, X. Liu, G. Yu, and W. Jiang · 2019
Closest in time.
UPSNet: A unified panoptic segmentation network
Y. Xiong, R. Liao, H. Zhao, R. Hu, M. Bai, E. Yumer, and R. Urtasun · 2019
Closest in time.
DeeperLab: Single-shot image parser
T.-J. Yang, M. D. Collins, Y. Zhu, J.-J. Hwang, T. Liu, X. Zhang, V. Sze, G. Papandreou, and L.-C. Chen · 2019
Closest in time.