Fetching the paper…
Reading the bibliography…
This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation.
Articulated pose estimation with flexible mixtures-of-parts
Y. Yang and D. Ramanan · 2011
Earlier work this paper cites.
Poselet conditioned pictorial structures
L. Pishchulin, M. Andriluka, P. V. Gehler, and B. Schiele · 2013
Earlier work this paper cites.
2d human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. V. Gehler, and B. Schiele · 2014
Earlier work this paper cites.
Articulated pose estimation by a graphical model with image dependent pairwise relations
X. Chen and A. L. Yuille · 2014
Earlier work this paper cites.
Locally scale-invariant convolutional neural networks
A. Kanazawa, A. Sharma, and D. W. Jacobs · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Microsoft COCO: common objects in context
T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Multi-source deep learning for human pose estimation
W. Ouyang, X. Chu, and X. Wang · 2014
Earlier work this paper cites.
Joint training of a convolutional network and a graphical model for human pose estimation
J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler · 2014
Earlier work this paper cites.
Deeppose: Human pose estimation via deep neural networks
A. Toshev and C. Szegedy · 2014
Earlier work this paper cites.
Scale-invariant convolutional neural networks
Y. Xu, T. Xiao, J. Zhang, K. Yang, and Z. Zhang · 2014
Earlier work this paper cites.
Combining local appearance and holistic view: Dual-source deep neural networks for human pose estimation
X. Fan, K. Zheng, Y. Lin, and S. Wang · 2015
Earlier work this paper cites.
Deeply-supervised nets
C. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu · 2015
Earlier work this paper cites.
Flowing convnets for human pose estimation in videos
T. Pfister, J. Charles, and A. Zisserman · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li · 2015
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Earlier work this paper cites.
Efficient object localization using convolutional networks
J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler · 2015
Earlier work this paper cites.
Holistically-nested edge detection
S. Xie and Z. Tu · 2015
Earlier work this paper cites.
Interlinked convolutional neural networks for face parsing
Y. Zhou, X. Hu, and B. Zhang · 2015
Earlier work this paper cites.
Human pose estimation via convolutional part heatmap regression
A. Bulat and G. Tzimiropoulos · 2016
Earlier work this paper cites.
A unified multi-scale deep convolutional neural network for fast object detection
Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos · 2016
Earlier work this paper cites.
Human pose estimation with iterative error feedback
J. Carreira, P. Agrawal, K. Fragkiadaki, and J. Malik · 2016
Earlier work this paper cites.
Structured feature learning for pose estimation
X. Chu, W. Ouyang, H. Li, and X. Wang · 2016
Earlier work this paper cites.
Chained predictions using convolutional neural networks
G. Gkioxari, A. Toshev, and N. Jaitly · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Bottom-up and top-down reasoning with hierarchical rectified gaussians
P. Hu and D. Ramanan · 2016
Earlier work this paper cites.
Deepercut: A deeper, stronger, and faster multi-person pose estimation model
E. Insafutdinov, L. Pishchulin, B. Andres, M. Andriluka, and B. Schiele · 2016
Earlier work this paper cites.
Human pose estimation using deep consensus voting
I. Lifshitz, E. Fetaya, and S. Ullman · 2016
Earlier work this paper cites.
MOT16: A benchmark for multi-object tracking
A. Milan, L. Leal-Taixé, I. D. Reid, S. Roth, and K. Schindler · 2016
Cited alongside, same era.
Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
Cited alongside, same era.
Deepcut: Joint subset partition and labeling for multi person pose estimation
L. Pishchulin, E. Insafutdinov, S. Tang, B. Andres, M. Andriluka, P. V. Gehler, and B. Schiele · 2016
Cited alongside, same era.
Convolutional neural fabrics
S. Saxena and J. Verbeek · 2016
Cited alongside, same era.
J. Wang, Z. Wei, T. Zhang, and W. Zeng · 2016
Cited alongside, same era.
Convolutional pose machines
Joint multi-person pose estimation and semantic part segmentation
F. Xia, P. Wang, X. Chen, and A. L. Yuille · 2017
Later among the works it cites.
Learning feature pyramids for human pose estimation
W. Yang, S. Li, W. Ouyang, H. Li, and X. Wang · 2017
Later among the works it cites.
Interleaved group convolutions
T. Zhang, G. Qi, B. Xiao, and J. Wang · 2017
Later among the works it cites.
Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
Later among the works it cites.
Multi-person pose estimation for posetrack with enhanced part affinity fields
X. Zhu, Y. Jiang, and Z. Luo · 2017
Later among the works it cites.
Posetrack: A benchmark for human pose estimation and tracking
M. Andriluka, U. Iqbal, A. Milan, E. Insafutdinov, L. Pishchulin, J. Gall, and B. Schiele · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Recurrent human pose estimgation
V. Belagiannis and A. Zisserman · 2017
Cited alongside, same era.
Realtime multi-person 2d pose estimation using part affinity fields
Z. Cao, T. Simon, S. Wei, and Y. Sheikh · 2017
Cited alongside, same era.
Adversarial posenet: A structure-aware convolutional network for human pose estimation
Y. Chen, C. Shen, X. Wei, L. Liu, and J. Yang · 2017
Cited alongside, same era.
Cascaded pyramid network for multi-person pose estimation
Y. Chen, Z. Wang, Y. Peng, Z. Zhang, G. Yu, and J. Sun · 2017
Cited alongside, same era.
Self adversarial training for human pose estimation
C. Chou, J. Chien, and H. Chen · 2017
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
Later among the works it cites.
Joint flow: Temporal flow fields for multi person tracking, 2018
A. Doering, U. Iqbal, and J. Gall · 2018
Later among the works it cites.
Detect-and-track: Efficient pose estimation in videos
R. Girdhar, G. Gkioxari, L. Torresani, M. Paluri, and D. Tran · 2018
Later among the works it cites.
Multi-scale structure-aware network for human pose estimation
L. Ke, M. Chang, H. Qi, and S. Lyu · 2018
Later among the works it cites.
Multiposenet: Fast multi-person pose estimation using pose residual network
M. Kocabas, S. Karagoz, and E. Akbas · 2018
Later among the works it cites.
Pose partition networks for multi-person pose estimation
X. Nie, J. Feng, J. Xing, and S. Yan · 2018
Later among the works it cites.
Human pose estimation with parsing induced learner
X. Nie, J. Feng, Y. Zuo, and S. Yan · 2018
Later among the works it cites.
Knowledge-guided deep fractal neural networks for human pose estimation
G. Ning, Z. Zhang, and Z. He · 2018
Later among the works it cites.
Personlab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model
G. Papandreou, T. Zhu, L.-C. Chen, S. Gidaris, J. Tompson, and K. Murphy · 2018
Later among the works it cites.
Jointly optimize data augmentation and network training: Adversarial data augmentation in human pose estimation
X. Peng, Z. Tang, F. Yang, R. S. Feris, and D. Metaxas · 2018
Later among the works it cites.
Nu-net: Deep residual wide field of view convolutional neural network for semantic segmentation
M. Samy, K. Amer, K. Eissa, M. Shaker, and M. ElHelw · 2018
Later among the works it cites.
Pose proposal networks
T. Sekii · 2018
Later among the works it cites.
IGCV3: interleaved low-rank group convolutions for efficient deep neural networks
K. Sun, M. Li, D. Liu, and J. Wang · 2018
Later among the works it cites.
Integral human pose regression
X. Sun, B. Xiao, F. Wei, S. Liang, and Y. Wei · 2018
Later among the works it cites.
Deeply learned compositional models for human pose estimation
W. Tang, P. Yu, and Y. Wu · 2018
Later among the works it cites.
Quantized densely connected u-nets for efficient landmark localization
Z. Tang, X. Peng, S. Geng, L. Wu, S. Zhang, and D. N. Metaxas · 2018
Later among the works it cites.
Mscoco keypoints challenge 2018
Z. Wang, W. Li, B. Yin, Q. Peng, T. Xiao, Y. Du, Z. Li, X. Zhang, G. Yu, and J. Sun · 2018
Later among the works it cites.
Simple baselines for human pose estimation and tracking
B. Xiao, H. Wu, and Y. Wei · 2018
Later among the works it cites.
Interleaved structured sparse convolutional neural networks
G. Xie, J. Wang, T. Zhang, J. Lai, R. Hong, and G. Qi · 2018
Later among the works it cites.
Pose flow: Efficient online pose tracking
Y. Xiu, J. Li, H. Wang, Y. Fang, and C. Lu · 2018
Later among the works it cites.
Deep convolutional neural networks with merge-and-run mappings
L. Zhao, M. Li, D. Meng, X. Li, Z. Zhang, Y. Zhuang, Z. Tu, and J. Wang · 2018
Later among the works it cites.