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Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition.
The representation and matching of pictorial structures
M. Fischler and R. Elschlager · 1973
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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
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Learning internal representations by error propagation
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
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Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel · 1989
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Original approach for the localisation of objects in images
R. Vaillant, C. Monrocq, and Y. LeCun · 1994
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Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
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Pictorial structures for object recognition
P. Felzenszwalb and D. Huttenlocher · 2005
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2007
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A comparison of statistical significance tests for information retrieval evaluation
M. D. Smucker, J. Allan, and B. Carterette · 2007
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Poselets: Body part detectors trained using 3d human pose annotations
L. Bourdev and J. Malik · 2009
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A theoretical analysis of feature pooling in visual recognition
Y.-L. Boureau, J. Ponce, and Y. LeCun · 2010
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Object detection with discriminatively trained part based models
P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan · 2010
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Learning fast approximations of sparse coding
K. Gregor and Y. LeCun · 2010
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Object detection with grammar models
R. Girshick, P. Felzenszwalb, and D. McAllester · 2011
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Learning hierarchical poselets for human parsing
Y. Wang, D. Tran, and Z. Liao · 2011
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Distance transforms of sampled functions
P. Felzenszwalb and D. Huttenlocher · 2012
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Beyond spatial pyramids: Receptive field learning for pooled image features
Y. Jia, C. Huang, and T. Darrell · 2012
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Maxout networks
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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Caffe: An open source convolutional architecture for fast feature embedding
Y. Jia · 2013
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Sketch tokens: A learned mid-level representation for contour and object detection
J. J. Lim, C. L. Zitnick, and P. Dollár · 2013
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Joint deep learning for pedestrian detection
W. Ouyang and X. Wang · 2013
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Histograms of sparse codes for object detection
X. Ren and D. Ramanan · 2013
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Selective search for object recognition
J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders · 2013
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Articulated human detection with flexible mixtures-of-parts
Y. Yang and D. Ramanan · 2012
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Deep neural networks for object detection
D. E. Christian Szegedy, Alexander Toshev · 2013
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Bottom-up segmentation for top-down detection
S. Fidler, R. Mottaghi, A. Yuille, and R. Urtasun · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2013
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Discriminatively trained deformable part models, release 5
R. Girshick, P. Felzenszwalb, and D. McAllester
Cited in the paper.
Regionlets for generic object detection
X. Wang, M. Yang, S. Zhu, and Y. Lin · 2013
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Densenet: Implementing efficient convnet descriptor pyramids
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OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
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