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We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation.
On seeing stuff: the perception of materials by humans and machines
E. H. Adelson · 2001
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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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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 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. Zitnick · 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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The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N. Cho, S. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
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Training deep neural networks on noisy labels with bootstrapping
S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich · 2014
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Relating things and stuff via objectproperty interactions
M. Sun, B.-s. 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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Convolutional feature masking for joint object and stuff segmentation
J. Dai, K. He, and J. Sun · 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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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 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.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Pixel-level encoding and depth layering for instance-level semantic segmentation
J. Uhrig, M. Cordts, U. Franke, and T. Brox · 2016
Cited alongside, same era.
A Unified Architecture for Instance and Semantic Segmentation
K. Alexander, H. Kaiming, G. Ross, and P. Dollár · 2017
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In-place activated batchnorm for memory-optimized training of dnns
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
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MSCOCO and Mapillary Panoptic Segmentation Challenge 2018
P. Chao, W. Jingbo, Y. Changqian, L. Xu, L. Huanyu, L. Zeming, Z. Yueqing, Z. Xiangyu, Y. Gang, and S. Jian · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
A. Kendall, Y. Gal, and R. Cipolla · 2018
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S. R. Bulò, L. Porzi, and P. Kontschieder · 2017
Cited alongside, same era.
Masklab: Instance segmentation by refining object detection with semantic and direction features
L.-C. Chen, A. Hermans, G. Papandreou, F. Schroff, P. Wang, and H. Adam · 2017
Cited alongside, same era.
Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Z. Chen, V. Badrinarayanan, C.-Y. Lee, and A. Rabinovich · 2017
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
I. Kokkinos · 2017
Cited alongside, same era.
Focal loss for dense object detection
T. Lin, P. Goyal, R. B. Girshick, K. He, and P. Dollár · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2017
Cited alongside, same era.
A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár · 2018
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Weakly- and semi-supervised panoptic segmentation
Q. Li, A. Arnab, and P. H. Torr · 2018
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Weakly-and semi-supervised panoptic segmentation
Q. Li, A. Arnab, and P. H. Torr · 2018
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Path aggregation network for instance segmentation
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia · 2018
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Effective use of synthetic data for urban scene semantic segmentation
F. S. Saleh, M. S. Aliakbarian, M. Salzmann, L. Petersson, and J. M. Alvarez · 2018
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Multi-task learning as multi-objective optimization
O. Sener and V. Koltun · 2018
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Multinet: Real-time joint semantic reasoning for autonomous driving
M. Teichmann, M. Weber, M. Zoellner, R. Cipolla, and R. Urtasun · 2018
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Y. Wu and K. He · 2018
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Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. Shen, L. Guibas, J. Malik, and S. Savarese · 2018
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