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Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks.
The application of bayesian methods for seeking the extremum
J. Mockus, V. Tiesis, and A. Zilinskas · 1978
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
A taxonomy of global optimization methods based on response surfaces
D. R. Jones · 2001
Earlier work this paper cites.
Face recognition with local binary patterns
T. Ahonen, A. Hadid, and M. Pietikäinen · 2004
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
D. G. Lowe · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
Earlier work this paper cites.
Large margin methods for structured and interdependent output variables
I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun · 2005
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
G. E. Hinton and R. R. Salakhutdinov · 2006
Earlier work this paper cites.
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
C. Rasmussen and C. Williams · 2006
Earlier work this paper cites.
Greedy layer-wise training of deep networks
Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, et al · 2007
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results, 2007
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2007
Earlier work this paper cites.
Learning to localize objects with structured output regression
M. B. Blaschko and C. H. Lampert · 2008
Earlier work this paper cites.
Sparse feature learning for deep belief networks
Y.-l. Boureau, Y. L. Cun, et al · 2008
Earlier work this paper cites.
Svm-struct: Support vector machine for complex outputs
T. Joachims · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Recognition using regions
C. Gu, J. J. Lim, P. Arbelaez, and J. Malik · 2009
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results, 2010
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan · 2010
Cited alongside, same era.
Unsupervised learning of hierarchical representations with convolutional deep belief networks
H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng · 2011
Cited alongside, same era.
Measuring the objectness of image windows
B. Alexe, T. Deselaers, and V. Ferrari · 2012
Cited alongside, same era.
Learning to localize detected objects
Q. Dai and D. Hoiem · 2012
Cited alongside, same era.
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results, 2012
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
Cited alongside, same era.
Are we ready for autonomous driving? the KITTI vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
Cited alongside, same era.
Deep neural networks for object detection
C. Szegedy, A. Toshev, and D. Erhan · 2013
Later among the works it cites.
Selective search for object recognition
J. R. R. Uijlings, K. E. A. Sande, T. Gevers, and A. W. M. Smeulders · 2013
Later among the works it cites.
Regionlets for generic object detection
X. Wang, M. Yang, S. Zhu, and Y. Lin · 2013
Later among the works it cites.
Scalable object detection using deep neural networks
D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov · 2014
Later among the works it cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Later among the works it cites.
Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
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Diagnosing error in object detectors
D. Hoiem, Y. Chodpathumwan, and Q. Dai · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Practical bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
Cited alongside, same era.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
Cited alongside, same era.
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 · 2013
Cited alongside, same era.
Bottom-up segmentation for top-down detection
S. Fidler, R. Mottaghi, A. Yuille, and R. Urtasun · 2013
Cited alongside, same era.
Later among the works it cites.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. B. Girshick, S. Guadarrama, and T. Darrell · 2014
Later among the works it cites.
ImageNet Large Scale Visual Recognition Challenge, 2014
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2014
Later among the works it cites.
Accurate object detection with joint classification-regression random forests
S. Schulter, C. Leistner, P. Wohlhart, P. M. Roth, and H. Bischof · 2014
Later among the works it cites.
Structured prediction for object detection in deep neural networks
H. Schulz and S. Behnke · 2014
Later among the works it cites.
OverFeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
Later among the works it cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Later among the works it cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Later among the works it cites.
Deep learning in neural networks: An overview
J. Schmidhuber · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Y. Zhang, K. Sohn, R. Villegas, G. Pan, and H. Lee · 2015
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