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Detecting small objects is notoriously challenging due to their low resolution and noisy representation.
Robust real-time face detection
P. Viola and M. J. Jones · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
Earlier work this paper cites.
Integral channel features
P. Dollár, Z. Tu, P. Perona, and S. Belongie · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Real time traffic sign detection using color and shape-based features
T. T. Le, S. T. Tran, S. Mita, and T. D. Nguyen · 2010
Earlier work this paper cites.
Traffic sign recognition with multi-scale convolutional networks
P. Sermanet and Y. LeCun · 2011
Earlier work this paper cites.
Pedestrian detection: An evaluation of the state of the art
P. Dollar, C. Wojek, B. Schiele, and P. Perona · 2012
Earlier work this paper cites.
A discriminative deep model for pedestrian detection with occlusion handling
W. Ouyang and X. Wang · 2012
Earlier work this paper cites.
Joint deep learning for pedestrian detection
W. Ouyang and X. Wang · 2013
Earlier work this paper cites.
Pedestrian detection with unsupervised multi-stage feature learning
P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun · 2013
Earlier work this paper cites.
Traffic sign detection based on convolutional neural networks
Y. Wu, Y. Liu, J. Li, H. Liu, and X. Hu · 2013
Earlier work this paper cites.
Ten years of pedestrian detection, what have we learned?
R. Benenson, M. Omran, J. Hosang, and B. Schiele · 2014
Earlier work this paper cites.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Earlier work this paper cites.
Fast feature pyramids for object detection
P. Dollár, R. Appel, S. Belongie, and P. Perona · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
Traffic sign recognition with hinge loss trained convolutional neural networks
J. Jin, K. Fu, and C. Zhang · 2014
Cited alongside, same era.
Local decorrelation for improved pedestrian detection
W. Nam, P. Dollár, and J. H. Han · 2014
Cited alongside, same era.
Strengthening the effectiveness of pedestrian detection with spatially pooled features
S. Paisitkriangkrai, C. Shen, and A. van den Hengel · 2014
Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2015
Later among the works it cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Later among the works it cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Later among the works it cites.
Pedestrian detection aided by deep learning semantic tasks
Y. Tian, P. Luo, X. Wang, and X. Tang · 2015
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Filtered channel features for pedestrian detection
S. Zhang, R. Benenson, and B. Schiele · 2015
Later among the works it cites.
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
S. Bell, C. L. Zitnick, K. Bala, and R. Girshick · 2015
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3d object proposals for accurate object class detection
X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun · 2015
Cited alongside, same era.
Deep generative image models using a laplacian pyramid of adversarial networks
E. L. Denton, S. Chintala, R. Fergus, et al · 2015
Cited alongside, same era.
Fast r-cnn
R. Girshick · 2015
Cited alongside, same era.
A novel plsa based traffic signs classification system
M. Haloi · 2015
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Learning complexity-aware cascades for deep pedestrian detection
M. S. Zhaowei Cai and N. Vasconcelos · 2015
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Object detection and counting with low quality videos
H. Jiang and S. Wang · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2016
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Combining markov random fields and convolutional neural networks for image synthesis
C. Li and M. Wand · 2016
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Deep relative distance learning: Tell the difference between similar vehicles
H. Liu, Y. Tian, Y. Yang, L. Pang, and T. Huang · 2016
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Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers
F. Yang, W. Choi, and Y. Lin · 2016
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Semantic image inpainting with perceptual and contextual losses
R. Yeh, C. Chen, T. Y. Lim, M. Hasegawa-Johnson, and M. N. Do · 2016
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Is faster r-cnn doing well for pedestrian detection?
L. Zhang, L. Lin, X. Liang, and K. He · 2016
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How far are we from solving pedestrian detection?
S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele · 2016
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Traffic-sign detection and classification in the wild
Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu · 2016
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