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Convolutional Neural Networks (CNNs) have recently emerged as the dominant model in computer vision.
Determining lightness from an image
Horn, B.K.P · 1974
Earlier work this paper cites.
Recovering intrinsic scene characteristics from images
Barrow, HG and Tenenbaum, JM · 1978
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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Fukushima, Kunihiko · 1980
Earlier work this paper cites.
Determining optical flow
Horn, Berthold K and Schunck, Brian G · 1981
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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
Earlier work this paper cites.
Transforming neural-net output levels to probability distributions
Denker, John and Lecun, Yann · 1991
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A practical bayesian framework for backpropagation networks
MacKay, David JC · 1992
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Shape-from-shading: a survey
Zhang, Ruo, Tsai, Ping-Sing, Cryer, James Edwin, and Shah, Mubarak · 1999
Earlier work this paper cites.
Ground truth dataset and baseline evaluations for intrinsic image algorithms
Grosse, Roger, Johnson, Micah K, Adelson, Edward H, and Freeman, William T · 2009
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Probabilistic graphical models: principles and techniques
Koller, Daphne and Friedman, Nir · 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, Rother, Carsten, and Criminisi, Antonio · 2009
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Piecewise training for structured prediction
Sutton, Charles and McCallum, Andrew · 2009
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A naturalistic open source movie for optical flow evaluation
Butler, Daniel J, Wulff, Jonas, Stanley, Garrett B, and Black, Michael J · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Estimation of intrinsic image sequences from image+ depth video
Lee, Kyong Joon, Zhao, Qi, Tong, Xin, Gong, Minmin, Izadi, Shahram, Lee, Sang Uk, Tan, Ping, and Lin, Stephen · 2012
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A simple model for intrinsic image decomposition with depth cues
Chen, Qifeng and Koltun, Vladlen · 2013
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Intrinsic images in the wild
Bell, Sean, Bala, Kavita, and Snavely, Noah · 2014
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Yarin and Ghahramani, Zoubin · 2015
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Deep structured output learning for unconstrained text recognition
Jaderberg, Max, Simonyan, Karen, Vedaldi, Andrea, and Zisserman, Andrew · 2015
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Adam: A method for stochastic optimization
Kingma, Diederik and Ba, Jimmy · 2015
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Liu, Fayao, Shen, Chunhua, and Lin, Guosheng · 2015
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Imagenet large scale visual recognition challenge
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Shape, illumination, and reflectance from shading
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Weight uncertainty in neural networks
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Chen, Liang-Chieh, Schwing, Alexander G, Yuille, Alan L, and Urtasun, Raquel · 2015
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Direct intrinsics: Learning albedo-shading decomposition by convolutional regression
Narihira, Takuya, Maire, Michael, and Yu, Stella X
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Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li · 2015
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Very deep convolutional networks for large-scale image recognition
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Conditional random fields as recurrent neural networks
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Zhou, Tinghui, Krähenbühl, Philipp, and Efros, Alexei A · 2015
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Learning ordinal relationships for mid-level vision
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