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Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation.
Multitask learning: A knowledge-based source of inductive bias
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Deep convolutional neural fields for depth estimation from a single image
F. Liu, C. Shen, and G. Lin · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 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
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Virtual worlds as proxy for multi-object tracking analysis
A. Gaidon, Q. Wang, Y. Cabon, and E. Vig · 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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Rethinking atrous convolution for semantic image segmentation
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
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CARLA: An open urban driving simulator
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun · 2017
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Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?
M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, K. Rosaen, and R. Vasudevan · 2017
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Find your way by observing the sun and other semantic cues
W.-C. Ma, S. Wang, M. A. Brubaker, S. Fidler, and R. Urtasun · 2017
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Playing for benchmarks
S. R. Richter, Z. Hayder, and V. Koltun · 2017
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Deeper depth prediction with fully convolutional residual networks
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab · 2016
Cited alongside, same era.
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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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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SegNet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 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
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S. Ruder · 2017
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A. Tsirikoglou, J. Kronander, M. Wrenninge, and J. Unger · 2017
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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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Evaluation of CNN-based single-image depth estimation methods
T. Koch, L. Liebel, F. Fraundorfer, and M. Körner · 2018
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Sim4CV: A photo-realistic simulator for computer vision applications
M. Müller, V. Casser, J. Lahoud, N. Smith, and B. Ghanem · 2018
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