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Training a semantic segmentation model requires a large amount of pixel-level annotation, hampering its application at scale.
Imagenet: A large-scale hierarchical image database
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
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Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J. Dai, K. He, and J. Sun · 2015
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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G. Papandreou, L.-C. Chen, K. Murphy, and A. L. Yuille · 2015
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Constrained convolutional neural networks for weakly supervised segmentation
D. Pathak, P. Krähenbühl, and T. Darrell · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
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What’s the point: Semantic segmentation with point supervision
A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei · 2016
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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
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Fcns in the wild: Pixel-level adversarial and constraint-based adaptation
Weakly supervised semantic segmentation using web-crawled videos
S. Hong, D. Yeo, S. Kwak, H. Lee, and B. Han · 2017
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
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RefineNet: Multi-path refinement networks for high-resolution semantic segmentation
G. Lin, A. Milan, C. Shen, and I. Reid · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Curriculum domain adaptation for semantic segmentation of urban scenes
Y. Zhang, P. David, and B. Gong · 2017
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Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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J. Hoffman, D. Wang, F. Yu, and T. Darrell · 2016
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Scribblesup: Scribble-supervised convolutional networks for semantic segmentation
D. Lin, J. Dai, J. Jia, K. He, and J. Sun · 2016
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Unsupervised domain adaptation with residual transfer networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Return of frustratingly easy domain adaptation
B. Sun, J. Feng, and K. Saenko · 2016
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Stc: A simple to complex framework for weakly-supervised semantic segmentation
Y. Wei, X. Liang, Y. Chen, X. Shen, M.-M. Cheng, J. Feng, Y. Zhao, and S. Yan · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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Adaptive semantic segmentation with a strategic curriculum of proxy labels
K. Chitta, J. Feng, and M. Hebert · 2018
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Adversarial learning for semi-supervised semantic segmentation
W.-C. Hung, Y.-H. Tsai, Y.-T. Liou, Y.-Y. Lin, and M.-H. Yang · 2018
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Strong baselines for neural semi-supervised learning under domain shift
S. Ruder and B. Plank · 2018
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Learning from synthetic data: Addressing domain shift for semantic segmentation
S. Sankaranarayanan, Y. Balaji, A. Jain, S. Lim, and R. Chellappa · 2018
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Bootstrapping the performance of webly supervised semantic segmentation
T. Shen, G. Lin, C. Shen, and I. Reid · 2018
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Learning to adapt structured output space for semantic segmentation
Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker · 2018
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Fully convolutional adaptation networks for semantic segmentation
Y. Zhang, Z. Qiu, T. Yao, D. Liu, and T. Mei · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Y. Zou, Z. Yu, B. V. Kumar, and J. Wang · 2018
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