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Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved impressive performance on a variety of classification tasks using purely feedforward processing.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
Earlier work this paper cites.
Snakes: Active contour models
M. Kass, A. Witkin, and D. Terzopoulos · 1988
Earlier work this paper cites.
Priming and human memory systems
E. Tulving and D. L. Schacter · 1990
Earlier work this paper cites.
Distributed Hierarchical Processing in the Primate Cerebral Cortex
D. J. Felleman and D. C. Van Essen · 1991
Earlier work this paper cites.
Stacked generalization
D. H. Wolpert · 1992
Earlier work this paper cites.
Active shape models-their training and application
T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham · 1995
Earlier work this paper cites.
Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons
J. M. Hupe, A. C. James, B. R. Payne, S. G. Lomber, P. Girard, and J. Bullier · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
the distinct modes of vision offered by feedforward and recurrent processing
V. A. F. Lamme and P. R. Roelfaema · 2000
Earlier work this paper cites.
Fast pose estimation with parameter-sensitive hashing
G. Shakhnarovich, P. Viola, and T. Darrell · 2003
Earlier work this paper cites.
Support vector machine learning for interdependent and structured output spaces
I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun · 2004
Earlier work this paper cites.
Stationary features and cat detection
F. Fleuret and D. Geman · 2007
Earlier work this paper cites.
Brain states: Top-down influences in sensory processing
C. D. Gilbert and M. Sigman · 2007
Earlier work this paper cites.
Auto-context and its application to high-level vision tasks
Z. Tu · 2008
Earlier work this paper cites.
Search-based structured prediction
H. Daumé III, J. Langford, and D. Marcu · 2009
Earlier work this paper cites.
Cascaded pose regression
P. Dollár, P. Welinder, and P. Perona · 2010
Earlier work this paper cites.
Clustered pose and nonlinear appearance models for human pose estimation
S. Johnson and M. Everingham · 2010
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Structured learning and prediction in computer vision
S. Nowozin and C. H. Lampert · 2011
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L. Sigal, M. Isard, H. Haussecker, and M. J. Black · 2011
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Articulated pose estimation with flexible mixtures-of-parts
Y. Yang and D. Ramanan · 2011
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Structured prediction cascades
D. Weiss, B. Sapp, and B. Taskar · 2012
Cited alongside, same era.
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
Later among the works it cites.
Iterated second-order label sensitive pooling for 3d human pose estimation
C. Ionescu, J. Carreira, and C. Sminchisescu · 2014
Later among the works it cites.
Deep structured output learning for unconstrained text recognition
M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Later among the works it cites.
Recurrent models of visual attention
V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu · 2014
Later among the works it cites.
Pose machines: Articulated pose estimation via inference machines
V. Ramakrishna, D. Munoz, M. Hebert, J. A. Bagnell, and Y. Sheikh · 2014
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
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D. Wyatte, T. Curran, and R. C. O’Reilly · 2012
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The ventral visual pathway: An expanded neural framework for the processing of object quality
B. C. U. L. M. M. Kravitz DJ, Saleem KS · 2013
Cited alongside, same era.
Fixed-point model for structured labeling
Q. Li, J. Wang, Z. Tu, and D. P. Wipf · 2013
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Poselet conditioned pictorial structures
L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele · 2013
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Strong appearance and expressive spatial models for human pose estimation
L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele · 2013
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Supervised descent method and its applications to face alignment
X. Xiong and F. De la Torre · 2013
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Pixels to voxels: Modeling visual representation in the human brain
P. Agrawal, D. Stansbury, J. Malik, and J. L. Gallant · 2014
Cited alongside, same era.
K. Simonyan and A. Zisserman · 2014
Later among the works it cites.
Deep networks with internal selective attention through feedback connections
M. F. Stollenga, J. Masci, F. Gomez, and J. Schmidhuber · 2014
Later among the works it cites.
Joint training of a convolutional network and a graphical model for human pose estimation
J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler · 2014
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Deeppose: Human pose estimation via deep neural networks
A. Toshev and C. Szegedy · 2014
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Matconvnet-convolutional neural networks for matlab
A. Vedaldi and K. Lenc · 2014
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