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In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task.
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A. Arnab, S. Jayasumana, S. Zheng, and P. H. Torr · 2016
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Attention to scale: Scale-aware semantic image segmentation
L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille · 2016
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The cityscapes dataset for semantic urban scene understanding
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J. Dai, K. He, Y. Li, S. Ren, and J. Sun · 2016
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
Dilated residual networks
F. Yu, V. Koltun, and T. A. Funkhouser · 2017
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M. Ren and R. S. Zemel · 2017
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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
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Panoptic segmentation with a joint semantic and instance segmentation network
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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Rethinking atrous convolution for semantic image segmentation
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
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Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Fully convolutional instance-aware semantic segmentation
Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei · 2017
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2017
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A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár · 2018
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Learning to fuse things and stuff
J. Li, A. Raventos, A. Bhargava, T. Tagawa, and A. Gaidon · 2018
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Weakly-and semi-supervised panoptic segmentation
Q. Li, A. Arnab, and P. H. Torr · 2018
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Attention-guided unified network for panoptic segmentation
Y. Li, X. Chen, Z. Zhu, L. Xie, G. Huang, D. Du, and X. Wang · 2018
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S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia · 2018
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T. Xiao, Y. Liu, B. Zhou, Y. Jiang, and J. Sun · 2018
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Icnet for real-time semantic segmentation on high-resolution images
H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia · 2018
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