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We characterize the problem of pose estimation for rigid objects in terms of determining viewpoint to explain coarse pose and keypoint prediction to capture the finer details.
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Imagenet: A large-scale hierarchical image database
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Detecting people using mutually consistent poselet activations
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Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
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Clustered pose and nonlinear appearance models for human pose estimation
S. Johnson and M. Everingham · 2010
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Viewpoint-aware object detection and pose estimation
D. Glasner, M. Galun, S. Alpert, R. Basri, and G. Shakhnarovich · 2011
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Articulated pose estimation with flexible mixtures-of-parts
Y. Yang and D. Ramanan · 2011
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Object detection using strongly-supervised deformable part models
H. Azizpour and I. Laptev · 2012
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
Multiscale combinatorial grouping
P. Arbeláez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik · 2014
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Is 2d information enough for viewpoint estimation?
A. Ghodrati, M. Pedersoli, and T. Tuytelaars · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Deformable part models are convolutional neural networks
R. B. Girshick, F. N. Iandola, T. Darrell, and J. Malik · 2014
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R-cnns for pose estimation and action detection
G. Gkioxari, B. Hariharan, R. Girshick, and J. Malik · 2014
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Using k-poselets for detecting people and localizing their keypoints
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