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State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification.
Learning representations by back-propagating errors
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A real-time algorithm for signal analysis with the help of the wavelet transform
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Backpropagation applied to handwritten zip code recognition
LeCun, Yann, Boser, Bernhard, Denker, John S., Henderson, Donnie, Howard, Richard E., Hubbard, Wayne, and Jackel, Lawrence D · 1989
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The discrete wavelet transform: wedding the à trous and Mallat algorithms
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Multiscale conditional random fields for image labeling
He, Xuming, Zemel, Richard S., and Carreira-Perpiñán, Miguel Á · 2004
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Semantic object classes in video: A high-definition ground truth database
Brostow, Gabriel J., Fauqueur, Julien, and Cipolla, Roberto · 2009
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Robust higher order potentials for enforcing label consistency
Kohli, Pushmeet, Ladicky, Lubor, and Torr, Philip H. S · 2009
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Associative hierarchical CRFs for object class image segmentation
Ladicky, Lubor, Russell, Christopher, Kohli, Pushmeet, and Torr, Philip H. S · 2009
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TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context
Shotton, Jamie, Winn, John M., Rother, Carsten, and Criminisi, Antonio · 2009
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Combining appearance and structure from motion features for road scene understanding
Sturgess, Paul, Alahari, Karteek, Ladicky, Lubor, and Torr, Philip H. S · 2009
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The Pascal visual object classes (VOC) challenge
Everingham, Mark, Gool, Luc J. Van, Williams, Christopher K. I., Winn, John M., and Zisserman, Andrew · 2010
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Context based object categorization: A critical survey
Galleguillos, Carolina and Belongie, Serge J · 2010
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Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
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Semantic contours from inverse detectors
Hariharan, Bharath, Arbelaez, Pablo, Bourdev, Lubomir D., Maji, Subhransu, and Malik, Jitendra · 2011
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Efficient inference in fully connected CRFs with Gaussian edge potentials
Krähenbühl, Philipp and Koltun, Vladlen · 2011
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ImageNet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Learning hierarchical features for scene labeling
Farabet, Clément, Couprie, Camille, Najman, Laurent, and LeCun, Yann · 2013
SegNet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling
Badrinarayanan, Vijay, Handa, Ankur, and Cipolla, Roberto · 2015
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Learning optical flow with convolutional neural networks
Fischer, Philipp, Dosovitskiy, Alexey, Ilg, Eddy, Häusser, Philip, Hazırbaş, Caner, Golkov, Vladimir, van der Smagt, Patrick, Cremers, Daniel, and Brox, Thomas · 2015
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A simple way to initialize recurrent networks of rectified linear units
Le, Quoc V., Jaitly, Navdeep, and Hinton, Geoffrey E · 2015
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Efficient piecewise training of deep structured models for semantic segmentation
Lin, Guosheng, Shen, Chunhua, Reid, Ian, and van dan Hengel, Anton · 2015
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Multiclass semantic video segmentation with object-level active inference
Liu, Buyu and He, Xuming · 2015
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Vision meets robotics: The KITTI dataset
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Conditional random fields as recurrent neural networks
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Caffe: Convolutional architecture for fast feature embedding
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Microsoft COCO: Common objects in context
Lin, Tsung-Yi, Maire, Michael, Belongie, Serge, Hays, James, Perona, Pietro, Ramanan, Deva, Dollár, Piotr, and Zitnick, C. Lawrence · 2014
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Semantic image segmentation with deep convolutional nets and fully connected CRFs
Chen, Liang-Chieh, Papandreou, George, Kokkinos, Iasonas, Murphy, Kevin, and Yuille, Alan L
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Fully convolutional networks for semantic segmentation
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Feature space optimization for semantic video segmentation
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