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We introduce the concept of a Visual Compiler that generates a scene specific pedestrian detector and pose estimator without any pedestrian observations.
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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Overview of the pets2006 challenge
D. Thirde, L. Li, and F. Ferryman · 2006
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Integral channel features
P. Dollár, Z. Tu, P. Perona, and S. Belongie · 2009
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Stable multi-target tracking in real-time surveillance video
B. Benfold and I. Reid · 2011
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Object detection with grammar models
R. Girshick, P. Felzenszwalb, and D. Mcallester · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Articulated people detection and pose estimation: Reshaping the future
L. Pishchulin, A. Jain, M. Andriluka, T. Thormählen, and B. Schiele · 2012
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Joint deep learning for pedestrian detection
W. Ouyang and X. Wang · 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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Efficient human pose estimation from single depth images
J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman, et al · 2013
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Articulated human detection with flexible mixtures of parts
Y. Yang and D. Ramanan · 2013
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Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
M. Aubry, D. Maturana, A. Efros, B. Russell, and J. Sivic · 2014
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Pose machines: Articulated pose estimation via inference machines
V. Ramakrishna, D. Munoz, M. Hebert, A. Bagnell, and Y. Sheikh · 2014
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Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
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Deeppose: Human pose estimation via deep neural networks
A. Toshev and C. Szegedy · 2014
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Virtual and real world adaptation for pedestrian detection
D. Vazquez, A. Lopez, J. Marin, D. Ponsa, and D. Geronimo · 2014
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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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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Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
H. Su, C. Qi, Y. Li, and L. Guibas · 2015
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Deep learning strong parts for pedestrian detection
Y. Tian, X. W. P. Luo, and X. Tang · 2015
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Filtered channel features for pedestrian detection
S. Zhang, R. Benenson, and B. Schiele · 2015
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Human pose estimation with iterative error feedback
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Learning complexity-aware cascades for deep pedestrian detection
Z. Cai, M. Saberian, and N. Vasconcelos · 2015
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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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Fast r-cnn
R. Girshick · 2015
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Learning scene-specific pedestrian detectors without real data
H. Hattori, V. Boddeti, K. Kitani, and T. Kanade · 2015
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Deep visual-semantic alignments for generating image descriptions
A. Karpathy and L. Fei-Fei · 2015
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J. Carreira, P. Agrawal, K. Fragkiadaki, and J. Malik · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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SSD: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, and C. Szegedy · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
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Convolutional pose machines
S. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh · 2016
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End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation
W. Yang, W. Ouyang, H. Li, and X. Wang · 2016
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