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Being a fundamental component in training and inference, data processing has not been systematically considered in human pose estimation community, to the best of our knowledge.
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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
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2014
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M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele, “2d human pose estimation: New benchmark and state of the art analysis,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 3686–3693
2014
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2014
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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
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O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision , vol. 115, no. 3, pp. 211–252, 2015
2015
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J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler, “Efficient object localization using convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 648–656
2015
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M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 3213–3223
2016
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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
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S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, “Convolutional pose machines,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4724–4732
2016
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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
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U. Iqbal and J. Gall, “Multi-person pose estimation with local joint-to-person associations,” in European Conference on Computer Vision . Springer, 2016, pp. 627–642
2016
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L. Pishchulin, E. Insafutdinov, S. Tang, B. Andres, M. Andriluka, P. V. Gehler, and B. Schiele, “Deepcut: Joint subset partition and labeling for multi person pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4929–4937
2016
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E. Insafutdinov, L. Pishchulin, B. Andres, M. Andriluka, and B. Schiele, “Deepercut: A deeper, stronger, and faster multi-person pose estimation model,” in European Conference on Computer Vision , 2016, pp. 34–50
2016
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 6, pp. 1137–1149, 2016
2016
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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
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J. Carreira and A. Zisserman, “Quo vadis, action recognition a new model and the kinetics dataset,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 6299–6308
2017
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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
2017
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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
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A. Newell, Z. Huang, and J. Deng, “Associative embedding: End-to-end learning for joint detection and grouping,” in Advances in Neural Information Processing Systems , 2017, pp. 2277–2287
2017
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T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , July 2017
2017
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
S. Huang, M. Gong, and D. Tao, “A coarse-fine network for keypoint localization,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 3028–3037
2017
Cited alongside, same era.
2019
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J. Li, C. Wang, H. Zhu, Y. Mao, H.-S. Fang, and C. Lu, “Crowdpose: Efficient crowded scenes pose estimation and a new benchmark,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 863–10 872
2019
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Z. Cao, G. Hidalgo Martinez, T. Simon, S. Wei, and Y. A. Sheikh, “Openpose: Realtime multi-person 2d pose estimation using part affinity fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019
2019
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X. Nie, J. Feng, J. Zhang, and S. Yan, “Single-stage multi-person pose machines,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 6951–6960
2019
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X. Liang, K. Gong, X. Shen, and L. Lin, “Look into person: Joint body parsing & pose estimation network and a new benchmark,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 4, pp. 871–885, 2018
2018
Cited alongside, same era.
S. Park, B. X. Nie, and S. C. Zhu, “Attribute and-or grammar for joint parsing of human pose, parts and attributes,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 7, pp. 1555–1569, 2018
2018
Cited alongside, same era.
K. Chen, P. Gabriel, A. Alasfour, C. Gong, W. K. Doyle, O. Devinsky, D. Friedman, P. Dugan, L. Melloni, T. Thesen et al. , “Patient-specific pose estimation in clinical environments,” IEEE journal of translational engineering in health and medicine , vol. 6, pp. 1–11, 2018
2018
Cited alongside, same era.
M. Andriluka, U. Iqbal, E. Insafutdinov, L. Pishchulin, A. Milan, J. Gall, and B. Schiele, “Posetrack: A benchmark for human pose estimation and tracking,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5167–5176
2018
Cited alongside, same era.
R. Girdhar, G. Gkioxari, L. Torresani, M. Paluri, and D. Tran, “Detect-and-track: Efficient pose estimation in videos,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , June 2018
2018
Cited alongside, same era.
B. Xiao, H. Wu, and Y. Wei, “Simple baselines for human pose estimation and tracking,” in European Conference on Computer Vision , 2018, pp. 466–481
2018
Cited alongside, same era.
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
Cited alongside, same era.
G. Papandreou, T. Zhu, L.-C. Chen, S. Gidaris, J. Tompson, and K. Murphy, “Personlab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model,” in European Conference on Computer Vision , 2018, pp. 269–286
2018
Cited alongside, same era.
T. Golda, T. Kalb, A. Schumann, and J. Beyerer, “Human pose estimation for real-world crowded scenarios,” in 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) . IEEE, 2019, pp. 1–8
2019
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2019
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K. Su, D. Yu, Z. Xu, X. Geng, and C. Wang, “Multi-person pose estimation with enhanced channel-wise and spatial information,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 5674–5682
2019
Closest in time.
S. Kreiss, L. Bertoni, and A. Alahi, “Pifpaf: Composite fields for human pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 977–11 986
2019
Closest in time.
G. Moon, J. Y. Chang, and K. M. Lee, “Posefix: Model-agnostic general human pose refinement network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7773–7781
2019
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2019
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2019
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H. Ci, X. Ma, C. Wang, and Y. Wang, “Locally connected network for monocular 3d human pose estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
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D. Luvizon, D. Picard, and H. Tabia, “Multi-task deep learning for real-time 3d human pose estimation and action recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
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K. Wang, L. Lin, C. Jiang, C. Qian, and P. Wei, “3d human pose machines with self-supervised learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 42, no. 5, pp. 1069–1082, 2020
2020
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M. Lu, K. Poston, A. Pfefferbaum, E. V. Sullivan, L. Fei-Fei, K. M. Pohl, J. C. Niebles, and E. Adeli, “Vision-based estimation of mds-updrs gait scores for assessing parkinson’s disease motor severity,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 , A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, and L. Joskowicz, Eds. Cham: Springer International Publishing, 2020, pp. 637–647
2020
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W. Chen, Z. Jiang, H. Guo, and X. Ni, “Fall detection based on key points of human-skeleton using openpose,” Symmetry , vol. 12, no. 5, p. 744, 2020
2020
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B. Cheng, B. Xiao, J. Wang, H. Shi, T. S. Huang, and L. Zhang, “Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 5386–5395
2020
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F. Zhang, X. Zhu, H. Dai, M. Ye, and C. Zhu, “Distribution-aware coordinate representation for human pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 7093–7102
2020
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J. Huang, Z. Zhu, F. Guo, and G. Huang, “The devil is in the details: Delving into unbiased data processing for human pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 5700–5709
2020
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J. Huang, Z. Shan, Y. Cai, F. Guo, Y. Ye, X. Chen, Z. Zhu, G. Huang, J. Lu, and D. Du, “Joint coco and lvis workshop at eccv 2020: Coco keypoint challenge track technical report: Udp++,” in ECCV Workshop , 2020
2020
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S. Jin, L. Xu, J. Xu, C. Wang, W. Liu, C. Qian, W. Ouyang, and P. Luo, “Whole-body human pose estimation in the wild,” in European Conference on Computer Vision . Springer, 2020, pp. 196–214
2020
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K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask r-cnn,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 42, no. 2, pp. 386–397, 2020
2020
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Y. Chen, C. Shen, H. Chen, X. Wei, L. Liu, and J. Yang, “Adversarial learning of structure-aware fully convolutional networks for landmark localization,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 42, no. 7, pp. 1654–1669, 2020
2020
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