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K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural networks , vol. 2, no. 5, pp. 359–366, 1989
1989
Earlier work this paper cites.
G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of control, signals and systems , vol. 2, no. 4, pp. 303–314, 1989
1989
Earlier work this paper cites.
B. Sapp, A. Toshev, and B. Taskar, “Cascaded models for articulated pose estimation,” in European conference on computer vision . Springer, 2010, pp. 406–420
2010
Earlier work this paper cites.
J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake, “Real-time human pose recognition in parts from single depth images,” in CVPR 2011 . Ieee, 2011, pp. 1297–1304
2011
Earlier work this paper cites.
Y. Yang and D. Ramanan, “Articulated pose estimation with flexible mixtures-of-parts,” in CVPR 2011 . IEEE, 2011, pp. 1385–1392
2011
Earlier work this paper cites.
C. Wang, Y. Wang, and A. L. Yuille, “An approach to pose-based action recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2013, pp. 915–922
2013
Earlier work this paper cites.
N.-G. Cho, A. L. Yuille, and S.-W. Lee, “Adaptive occlusion state estimation for human pose tracking under self-occlusions,” Pattern Recognition , vol. 46, no. 3, pp. 649–661, 2013
2013
Earlier work this paper cites.
L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele, “Poselet conditioned pictorial structures,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2013, pp. 588–595
2013
Earlier work this paper cites.
——, “Strong appearance and expressive spatial models for human pose estimation,” in Proceedings of the IEEE international conference on Computer Vision , 2013, pp. 3487–3494
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
A. Toshev and C. Szegedy, “Deeppose: Human pose estimation via deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 1653–1660
2014
Earlier work this paper cites.
J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler, “Joint training of a convolutional network and a graphical model for human pose estimation,” in Advances in neural information processing systems , 2014, pp. 1799–1807
2014
Earlier work this paper cites.
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele, “2d human pose estimation: New benchmark and state of the art analysis,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
G. Chéron, I. Laptev, and C. Schmid, “P-cnn: Pose-based cnn features for action recognition,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 3218–3226
2015
Cited alongside, same era.
A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” in European conference on computer vision . Springer, 2016, pp. 483–499
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
J. Carreira, P. Agrawal, K. Fragkiadaki, and J. Malik, “Human pose estimation with iterative error feedback,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4733–4742
2016
Cited alongside, same era.
G. Papandreou, T. Zhu, N. Kanazawa, A. Toshev, J. Tompson, C. Bregler, and K. Murphy, “Towards accurate multi-person pose estimation in the wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 4903–4911
2017
Later among the works it cites.
H.-S. Fang, S. Xie, Y.-W. Tai, and C. Lu, “Rmpe: Regional multi-person pose estimation,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 2334–2343
2017
Later among the works it cites.
W. Yang, S. Li, W. Ouyang, H. Li, and X. Wang, “Learning feature pyramids for human pose estimation,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1281–1290
2017
Later among the works it cites.
B. Xiao, H. Wu, and Y. Wei, “Simple baselines for human pose estimation and tracking,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 466–481
2018
Later among the works it cites.
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I. Lifshitz, E. Fetaya, and S. Ullman, “Human pose estimation using deep consensus voting,” in European Conference on Computer Vision . Springer, 2016, pp. 246–260
2016
Cited alongside, same era.
W. Yang, W. Ouyang, H. Li, and X. Wang, “End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 3073–3082
2016
Cited alongside, same era.
U. Rafi, B. Leibe, J. Gall, and I. Kostrikov, “An efficient convolutional network for human pose estimation.” in BMVC , vol. 1, 2016, p. 2
2016
Cited alongside, same era.
Z. Yang, X. He, J. Gao, L. Deng, and A. Smola, “Stacked attention networks for image question answering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 21–29
2016
Cited alongside, same era.
Y. Chen, C. Shen, X.-S. Wei, L. Liu, and J. Yang, “Adversarial posenet: A structure-aware convolutional network for human pose estimation,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1212–1221
2017
Cited alongside, same era.
X. Chu, W. Yang, W. Ouyang, C. Ma, A. L. Yuille, and X. Wang, “Multi-context attention for human pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1831–1840
2017
Cited alongside, same era.
A. Bulat and G. Tzimiropoulos, “Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 3706–3714
2017
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
Cited alongside, same era.
L. Ke, M.-C. Chang, H. Qi, and S. Lyu, “Multi-scale structure-aware network for human pose estimation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 713–728
2018
Later among the works it cites.
Y. Chen, Z. Wang, Y. Peng, Z. Zhang, G. Yu, and J. Sun, “Cascaded pyramid network for multi-person pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7103–7112
2018
Later among the works it cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Later among the works it cites.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7794–7803
2018
Later among the works it cites.
X. Sun, B. Xiao, F. Wei, S. Liang, and Y. Wei, “Integral human pose regression,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 529–545
2018
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2019
Closest in time.
K. Sun, B. Xiao, D. Liu, and J. Wang, “Deep high-resolution representation learning for human pose estimation,” in CVPR , 2019
2019
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2019
Closest in time.
D. C. Luvizon, H. Tabia, and D. Picard, “Human pose regression by combining indirect part detection and contextual information,” Computers & Graphics , vol. 85, pp. 15–22, 2019
2019
Closest in time.