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We propose a direct, regression-based approach to 2D human pose estimation from single images.
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Sun, X., Shang, J., Liang, S., Wei, Y.: Compositional human pose regression. In: Proc. IEEE Int. Conf. Comp. Vis. pp. 2602–2611 (2017)
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proc. Advances in Neural Inf. Process. Syst. pp. 5998–6008 (2017)
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Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn. pp. 7103–7112 (2018)
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Sun, X., Xiao, B., Wei, F., Liang, S., Wei, Y.: Integral human pose regression. In: Proc. Eur. Conf. Comp. Vis. pp. 529–545 (2018)
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Li, W., Wang, Z., Yin, B., Peng, Q., Du, Y., Xiao, T., Yu, G., Lu, H., Wei, Y., Sun, J.: Rethinking on multi-stage networks for human pose estimation. arXiv: Comp. Res. Repository (2019)
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Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: Proc. Int. Conf. Learn. Representations (2019)
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Huang, J., Zhu, Z., Guo, F., Huang, G.: The devil is in the details: Delving into unbiased data processing for human pose estimation. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn. pp. 5700–5709 (2020)
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Zhang, F., Zhu, X., Dai, H., Ye, M., Zhu, C.: Distribution-aware coordinate representation for human pose estimation. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn. (June 2020)
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Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn. pp. 5693–5703 (2019)
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Tian, Z., Chen, H., Shen, C.: Directpose: Direct end-to-end multi-person pose estimation. arXiv: Comp. Res. Repository (2019)
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Cai, Y., Wang, Z., Luo, Z., Yin, B., Du, A., Wang, H., Zhang, X., Zhou, X., Zhou, E., Sun, J.: Learning delicate local representations for multi-person pose estimation. In: Proc. Eur. Conf. Comp. Vis. pp. 455–472. Springer (2020)
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Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. Eur. Conf. Comp. Vis. pp. 213–229. Springer (2020)
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Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T.S., Zhang, L.: Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn. pp. 5386–5395 (2020)
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Gu, K., Yang, L., Yao, A.: Removing the bias of integral pose regression. In: Proc. IEEE Int. Conf. Comp. Vis. pp. 11067–11076 (2021)
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Li, J., Bian, S., Zeng, A., Wang, C., Pang, B., Liu, W., Lu, C.: Human pose regression with residual log-likelihood estimation. In: Proc. IEEE Int. Conf. Comp. Vis. (2021)
2021
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Li, J., Bian, S., Zeng, A., Wang, C., Pang, B., Liu, W., Lu, C.: Human pose regression with residual log-likelihood estimation. In: Proc. IEEE Int. Conf. Comp. Vis. (2021)
2021
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Li, K., Wang, S., Zhang, X., Xu, Y., Xu, W., Tu, Z.: Pose recognition with cascade transformers. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn. pp. 1944–1953 (2021)
2021
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Li, Y., Zhang, S., Wang, Z., Yang, S., Yang, W., Xia, S.T., Zhou, E.: TokenPose: Learning keypoint tokens for human pose estimation. In: Proc. IEEE Int. Conf. Comp. Vis. (2021)
2021
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Yang, S., Quan, Z., Nie, M., Yang, W.: TransPose: Keypoint localization via Transformer. In: Proc. IEEE Int. Conf. Comp. Vis. (2021)
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Yuan, Y., Fu, R., Huang, L., Lin, W., Zhang, C., Chen, X., Wang, J.: HRFormer: High-resolution transformer for dense prediction. In: Proc. Advances in Neural Inf. Process. Syst. (2021)
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Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: Deformable Transformers for end-to-end object detection. In: Proc. Int. Conf. Learn. Representations (2021)
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