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Segmenting semantic objects from images and parsing them into their respective semantic parts are fundamental steps towards detailed object understanding in computer vision.
Using recognition to guide a robot’s attention
A. Thomas, V. Ferrari, B. Leibe, T. Tuytelaars, and L. J. V. Gool · 2008
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Poselets: Body part detectors trained using 3d human pose annotations
L. D. Bourdev and J. Malik · 2009
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An empirical study of context in object detection
S. K. Divvala, D. Hoiem, J. Hays, A. A. Efros, and M. Hebert · 2009
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The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. A. McAllester, and D. Ramanan · 2010
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Auto-context and its application to high-level vision tasks and 3d brain image segmentation
Z. Tu and X. Bai · 2010
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Part and appearance sharing: Recursive compositional models for multi-view
L. Zhu, Y. Chen, A. Torralba, W. T. Freeman, and A. L. Yuille · 2010
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Shape-based pedestrian parsing
Y. Bo and C. C. Fowlkes · 2011
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Object detection with grammar models
R. B. Girshick, P. F. Felzenszwalb, and D. A. McAllester · 2011
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Object detection and segmentation from joint embedding of parts and pixels
M. Maire, S. X. Yu, and P. Perona · 2011
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Shared parts for deformable part-based models
P. Ott and M. Everingham · 2011
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The truth about cats and dogs
O. M. Parkhi, A. Vedaldi, C. V. Jawahar, and A. Zisserman · 2011
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Articulated part-based model for joint object detection and pose estimation
M. Sun and S. Savarese · 2011
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Articulated pose estimation with flexible mixtures-of-parts
Y. Yang and D. Ramanan · 2011
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Max margin learning of hierarchical configural deformable templates (hcdts) for efficient object parsing and pose estimation
L. Zhu, Y. Chen, C. Lin, and A. L. Yuille · 2011
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Semantic segmentation using regions and parts
P. Arbelaez, B. Hariharan, C. Gu, S. Gupta, L. D. Bourdev, and J. Malik · 2012
Cited alongside, same era.
Object detection using strongly-supervised deformable part models
H. Azizpour and I. Laptev · 2012
Cited alongside, same era.
Semantic segmentation with second-order pooling
J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu · 2012
Cited alongside, same era.
A generative model for parts-based object segmentation
S. M. A. Eslami and C. K. I. Williams · 2012
Cited alongside, same era.
Multi-component models for object detection
C. Gu, P. A. Arbeláez, Y. Lin, K. Yu, and J. Malik · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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
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Parsing semantic parts of cars using graphical models and segment appearance consistency
W. Lu, X. Lian, and A. L. Yuille · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Deeppose: Human pose estimation via deep neural networks
A. Toshev and C. Szegedy · 2014
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L. Wan, D. Eigen, and R. Fergus · 2014
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Efficient closed-form solution to generalized boundary detection
M. Leordeanu, R. Sukthankar, and C. Sminchisescu · 2012
Cited alongside, same era.
Parsing clothing in fashion photographs
K. Yamaguchi, M. H. Kiapour, L. E. Ortiz, and T. L. Berg · 2012
Cited alongside, same era.
Loopy belief propagation for approximate inference: An empirical study
K. P. Murphy, Y. Weiss, and M. I. Jordan · 2013
Cited alongside, same era.
Multiscale combinatorial grouping
P. A. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marqués, and J. Malik · 2014
Cited alongside, same era.
Detect what you can: Detecting and representing objects using holistic models and body parts
X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. L. Yuille · 2014
Cited alongside, same era.
Context as supervisory signal: Discovering objects with predictable context
C. Doersch, A. Gupta, and A. A. Efros · 2014
Cited alongside, same era.
Later among the works it cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Part-based r-cnns for fine-grained category detection
N. Zhang, J. Donahue, R. B. Girshick, and T. Darrell · 2014
Later among the works it cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille · 2015
Closest in time.
Deformable part models are convolutional neural networks
R. Girshick, F. Iandola, T. Darrell, and J. Malik · 2015
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Efficient piecewise training of deep structured models for semantic segmentation, 2015
G. Lin, C. Shen, I. Reid, and A. van dan Hengel · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Semantic part segmentation using compositional model combining shape and appearance
J. Wang and A. Yuille · 2015
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Towards unified depth and semantic prediction from a single image
P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A. Yuille · 2015
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segdeepm: Exploiting segmentation and context in deep neural networks for object detection
Y. Zhu, R. Urtasun, R. Salakhutdinov, and S. Fidler · 2015
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