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As autonomous vehicles become an every-day reality, high-accuracy pedestrian detection is of paramount practical importance.
Rapid object detection using a boosted cascade of simple features
P. Viola and M. Jones · 2001
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An extended set of haar-like features for rapid object detection
R. Lienhart and J. Maydt · 2002
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Inferring 3d structure with a statistical image-based shape model
K. Grauman, G. Shakhnarovich, and T. Darrell · 2003
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Model-based validation approaches and matching techniques for automotive vision based pedestrian detection
A. Broggi, A. Fascioli, P. Grisleri, T. Graf, and M. Meinecke · 2005
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Inria person dataset, 2005
N. Dalal and B. Triggs · 2005
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A local basis representation for estimating human pose from cluttered images
A. Agarwal and B. Triggs · 2006
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3D game engine design: a practical approach to real-time computer graphics
D. H. Eberly · 2006
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Fast human detection using a cascade of histograms of oriented gradients
Q. Zhu, M.-C. Yeh, K.-T. Cheng, and S. Avidan · 2006
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A mobile vision system for robust multi-person tracking
A. Ess, B. Leibe, K. Schindler, , and L. van Gool · 2008
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Nearest neighbor search methods for handshape recognition
M. Potamias and V. Athitsos · 2008
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Pedestrian detection: A benchmark
P. Dollár, C. Wojek, B. Schiele, and P. Perona · 2009
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Monocular pedestrian detection: Survey and experiments
M. Enzweiler and D. M. Gavrila · 2009
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Multi-cue onboard pedestrian detection
C. Wojek, S. Walk, and B. Schiele · 2009
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A database-based framework for gesture recognition
V. Athitsos, H. Wang, and A. Stefan · 2010
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Learning appearance in virtual scenarios for pedestrian detection
J. Marin, D. Vázquez, D. Gerónimo, and A. M. López · 2010
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Hands in action: real-time 3d reconstruction of hands in interaction with objects
J. Romero, H. Kjellström, and D. Kragic · 2010
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Learning people detection models from few training samples
L. Pishchulin, A. Jain, C. Wojek, M. Andriluka, T. Thormählen, and B. Schiele · 2011
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Pedestrian detection: An evaluation of the state of the art
P. Dollár, C. Wojek, B. Schiele, and P. Perona · 2012
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Teaching 3d geometry to deformable part models
B. Pepik, M. Stark, P. Gehler, and B. Schiele · 2012
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Data-driven scene understanding from 3d models
S. Satkin, J. Lin, and M. Hebert · 2012
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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2d human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
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Fast feature pyramids for object detection
P. Dollár, R. Appel, S. Belongie, and P. Perona · 2014
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Flownet: Learning optical flow with convolutional networks
P. Fischer, A. Dosovitskiy, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
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Learning scene-specific pedestrian detectors without real data
H. Hattori, V. Naresh Boddeti, K. M. Kitani, and T. Kanade · 2015
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Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
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N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 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
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Analysis by synthesis: 3d object recognition by object reconstruction
M. Hejrati and D. Ramanan · 2014
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Unsupervised feature learning for 3d scene labeling
K. Lai, L. Bo, and D. Fox · 2014
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Opendr: An approximate differentiable renderer
M. M. Loper and M. J. Black · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2016
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Learning physical intuition of block towers by example
A. Lerer, S. Gross, and R. Fergus · 2016
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Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 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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