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Recent attempts for unsupervised landmark learning leverage synthesized image pairs that are similar in appearance but different in poses.
Unsupervised disentanglement of pose, appearance and background from images and videos
A. Dundar, K. J. Shih, A. Garg, R. Pottorf, A. Tao, and B. Catanzaro · 2001
Earlier work this paper cites.
Unsupervised disentanglement of pose, appearance and background from images and videos
A. Dundar, K. J. Shih, A. Garg, R. Pottorf, A. Tao, and B. Catanzaro · 2001
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
G. E. Hinton and R. R. Salakhutdinov · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization
M. Koestinger, P. Wohlhart, P. M. Roth, and H. Bischof · 2011
Earlier work this paper cites.
Disentangling factors of variation via generative entangling
G. Desjardins, A. Courville, and Y. Bengio · 2012
Earlier work this paper cites.
Robust face landmark estimation under occlusion
X. P. Burgos-Artizzu, P. Perona, and P. Dollár · 2013
Earlier work this paper cites.
Deep convolutional network cascade for facial point detection
Y. Sun, X. Wang, and X. Tang · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Hierarchical recurrent neural network for skeleton based action recognition
Y. Du, W. Wang, and L. Wang · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Learning deep representation for face alignment with auxiliary attributes
Z. Zhang, P. Luo, C. C. Loy, and X. Tang · 2015
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Convolutional neural network architecture for geometric matching
I. Rocco, R. Arandjelovic, and J. Sivic · 2017
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Unsupervised learning of object landmarks through conditional image generation
T. Jakab, A. Gupta, H. Bilen, and A. Vedaldi · 2018
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Recurrent transformer networks for semantic correspondence
S. Kim, S. Lin, S. R. JEON, D. Min, and K. Sohn · 2018
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Diverse image-to-image translation via disentangled representations
H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. K. Singh, and M.-H. Yang · 2018
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Attentive semantic alignment with offset-aware correlation kernels
P. H. Seo, J. Lee, D. Jung, B. Han, and M. Cho · 2018
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Spatial temporal graph convolutional networks for skeleton-based action recognition
S. Yan, Y. Xiong, and D. Lin · 2018
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
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Realtime multi-person 2d pose estimation using part affinity fields
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh · 2017
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Unsupervised learning of disentangled representations from video
E. L. Denton et al · 2017
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Proposal flow: Semantic correspondences from object proposals
B. Ham, M. Cho, C. Schmid, and J. Ponce · 2017
Cited alongside, same era.
End-to-end weakly-supervised semantic alignment
I. Rocco, R. Arandjelović, and J. Sivic
Cited in the paper.
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Unsupervised discovery of object landmarks as structural representations
Y. Zhang, Y. Guo, Y. Jin, Y. Luo, Z. He, and H. Lee · 2018
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Joint learning of semantic alignment and object landmark detection
S. Jeon, D. Min, S. Kim, and K. Sohn · 2019
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Unsupervised part-based disentangling of object shape and appearance
D. Lorenz, L. Bereska, T. Milbich, and B. Ommer · 2019
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Unsupervised learning of object structure and dynamics from videos
M. Minderer, C. Sun, R. Villegas, F. Cole, K. P. Murphy, and H. Lee · 2019
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Unsupervised learning of landmarks by descriptor vector exchange
J. Thewlis, S. Albanie, H. Bilen, and A. Vedaldi · 2019
Later among the works it cites.