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There has been significant progress on pose estimation and increasing interests on pose tracking in recent years.
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. Journal on Image and Video Processing 2008
2008
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)
2012
Earlier work this paper cites.
Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 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.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in neural information processing systems. pp. 1799–1807 (2014)
2014
Earlier work this paper cites.
Toshev, A., Szegedy, C.: Deeppose: Human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1653–1660 (2014)
2014
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. pp. 448–456 (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. ICLR (2015)
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp. 91–99 (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Jifeng Dai, Yi Li, K.H., Sun, J.: R-FCN: Object detection via region-based fully convolutional networks. In: NIPS (2016)
2016
Cited alongside, same era.
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: European Conference on Computer Vision. pp. 483–499. Springer (2016)
2016
Cited alongside, same era.
Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P.V., Schiele, B.: Deepcut: Joint subset partition and labeling for multi person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4929–4937 (2016)
2016
Cited alongside, same era.
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: CVPR (2017)
2017
Cited alongside, same era.
Papandreou, G., Zhu, T., Kanazawa, N., Toshev, A., Tompson, J., Bregler, C., Murphy, K.: Towards accurate multi-person pose estimation in the wild. In: Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. pp. 3711–3719. IEEE (2017)
2017
Later among the works it cites.
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI. vol. 4, p. 12 (2017)
2017
Later among the works it cites.
Yang, W., Li, S., Ouyang, W., Li, H., Wang, X.: Learning feature pyramids for human pose estimation. In: IEEE International Conference on Computer Vision (2017)
2017
Later among the works it cites.
Zhu, X., Jiang, Y., Luo, Z.: Multi-person pose estimation for posetrack with enhanced part affinity fields. In: ICCV PoseTrack Workshop (2017)
2017
Later among the works it cites.
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Chen, Y., Shen, C., Wei, X.S., Liu, L., Yang, J.: Adversarial posenet: A structure-aware convolutional network for human pose estimation. In: IEEE International Conference on Computer Vision. pp. 1212–1221 (2017)
2017
Cited alongside, same era.
Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A.L., Wang, X.: Multi-context attention for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1831–1840 (2017)
2017
Cited alongside, same era.
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 764–773 (2017)
2017
Cited alongside, same era.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Computer Vision (ICCV), 2017 IEEE International Conference on. pp. 2980–2988. IEEE (2017)
2017
Cited alongside, same era.
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). vol. 2 (2017)
2017
Cited alongside, same era.
Jin, S., Ma, X., Han, Z., Wu, Y., Yang, W., Liu, W., Qian, C., Ouyang, W.: Towards multi-person pose tracking: Bottom-up and top-down methods. In: ICCV PoseTrack Workshop (2017)
2017
Cited alongside, same era.
Newell, A., Huang, Z., Deng, J.: Associative embedding: End-to-end learning for joint detection and grouping. In: Advances in Neural Information Processing Systems. pp. 2274–2284 (2017)
2017
Cited alongside, same era.
Deformable-ConvNet. https://github.com/msracver/Deformable-ConvNets
Cited in the paper.
Zhu, X., Wang, Y., Dai, J., Yuan, L., Wei, Y.: Flow-guided feature aggregation for video object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). pp. 408–417. IEEE (2017)
2017
Later among the works it cites.
Zhu, X., Xiong, Y., Dai, J., Yuan, L., Wei, Y.: Deep feature flow for video recognition. In: Proc. CVPR. vol. 2, p. 7 (2017)
2017
Later among the works it cites.
Andriluka, M., Iqbal, U., Milan, A., Insafutdinov, E., Pishchulin, L., Gall, J., Schiele, B.: Posetrack: A benchmark for human pose estimation and tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5167–5176 (2018)
2018
Closest in time.
Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: CVPR (2018)
2018
Closest in time.
Girdhar, R., Gkioxari, G., Torresani, L., Paluri, M., Tran, D.: Detect-and-track: Efficient pose estimation in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 350–359 (2018)
2018
Closest in time.
NVIDIA: flownet2-pytorch. https://github.com/NVIDIA/flownet2-pytorch
2018
Closest in time.