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We address the problem of detecting people in natural scenes using a part approach based on poselets.
Handwritten digit recognition with a back-propagation network
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1990
Earlier work this paper cites.
Rapid object detection using a boosted cascade of simple features
P. Viola and M. Jones · 2001
Earlier work this paper cites.
Object class recognition by unsupervised scale-invariant learning
R. Fergus, P. Perona, and A. Zisserman · 2003
Earlier work this paper cites.
Combined object categorization and segmentation with an implicit shape model
B. Leibe, A. Leonardis, and B. Schiele · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
Earlier work this paper cites.
Poselets: Body part detectors trained using 3D human pose annotations
L. Bourdev and J. Malik · 2009
Earlier work this paper cites.
Detecting people using mutually consistent poselet activations
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The fastest pedestrian detector in the west
P. Dollár, S. Belongie, and P. Perona · 2010
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J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders · 2013
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Regionlets for generic object detection
X. Wang, M. Yang, S. Zhu, and Y. Lin · 2013
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Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, I. Laptev, L. Bottou, and J. Sivic · 2014
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Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
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PANDA: Pose Aligned Networks for Deep Attribute Modeling
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http://pascallin.ecs.soton.ac.uk/challenges/VOC/
The pascal visual object classes challenge
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik
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