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Simple Light-Weight Network for Human Pose Estimation

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PRICAI 2021: Trends in Artificial Intelligence (PRICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13033))

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Abstract

Human pose estimation has achieved significant improvement. However, most existing methods mainly consider how to improve the model performance using complex architecture or computationally expensive model, ignoring the deployment costs in practice, especially in human-robot interaction. In this paper, we investigate a highly efficient pose estimation model with comparable accuracy. We propose an adaptive convolution, which can adaptively generate one or more feature maps with desired channels. Since redundant information in the feature map is an important characteristic, to preserve the redundant information while taking only a few numbers of FLOPs and parameters, we propose a light-weight block based on adaptive convolution, which is performed with two parallel convolution operations. And then, to further reduce the FLOPs, we propose heterogeneous filters based light-weight block, which contains two different kinds of filters in each layer. Finally, three light-weight units are designed to stack light-weight block, and a simple light-weight pose estimation network (SLPE) can be easily established. Extensive evaluations demonstrate the advantages of SLPE over state-of-the-art methods in terms of model cost-effectiveness on the standard benchmark datasets, MPII and COCO dataset.

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Notes

  1. 1.

    If one parallel step is converted to multiple sequential steps, it means increasing the latency. Because all computations have to be done sequentially across layers, the latter layer needs to be executed after the previous layer is executed.

References

  1. Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2d human pose estimation: new benchmark and state of the art analysis. In: CVPR, pp. 3686–3693 (2014)

    Google Scholar 

  2. Bulat, A., Tzimiropoulos, G.: Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources. In: ICCV, pp. 3706–3714 (2017)

    Google Scholar 

  3. Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: CVPR, pp. 7291–7299 (2017)

    Google Scholar 

  4. Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: CVPR, pp. 7103–7112 (2018)

    Google Scholar 

  5. Chen, Y., et al.: Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution. In: ICCV, pp. 3435–3444 (2019)

    Google Scholar 

  6. Chéron, G., Laptev, I., Schmid, C.: P-CNN: pose-based CNN features for action recognition. In: ICCV, pp. 3218–3226 (2015)

    Google Scholar 

  7. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR, pp. 1251–1258 (2017)

    Google Scholar 

  8. Fang, H.-S., Xie, S., Tai, Y.-W., Lu, C.: RMPE: regional multi-person pose estimation. In: ICCV, pp. 2334–2343 (2017)

    Google Scholar 

  9. Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. In: ICLR, pp. 1–14 (2016)

    Google Scholar 

  10. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV, pp. 2961–2969 (2017)

    Google Scholar 

  11. He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. In: IJCAI, pp. 2234–2240 (2018)

    Google Scholar 

  12. Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

  13. Huang, S., Gong, M., Tao, D.: A coarse-fine network for keypoint localization. In: ICCV, pp. 3028–3037 (2017)

    Google Scholar 

  14. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR, pp. 1–15 (2015)

    Google Scholar 

  15. Kocabas, M., Karagoz, S., Akbas, E.: MultiPoseNet: fast multi-person pose estimation using pose residual network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 437–453. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_26

    Chapter  Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)

    Google Scholar 

  17. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  18. Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 122–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_8

    Chapter  Google Scholar 

  19. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29

    Chapter  Google Scholar 

  20. Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR, pp. 5693–5703 (2019)

    Google Scholar 

  21. Tang, W., Yu, P., Wu, Y.: Deeply learned compositional models for human pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 197–214. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_12

    Chapter  Google Scholar 

  22. Wan, B., Zhou, D., Liu, Y., Li, R., He, X.: Pose-aware multi-level feature network for human object interaction detection. In: ICCV, pp. 9469–9478 (2019)

    Google Scholar 

  23. Wang, C., Wang, Y., Yuille, A.L.: An approach to pose-based action recognition. In: CVPR, pp. 915–922 (2013)

    Google Scholar 

  24. Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 472–487. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_29

    Chapter  Google Scholar 

  25. Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: AAAI, pp. 7444–7452 (2018)

    Google Scholar 

  26. Yang, W., Li, S., Ouyang, W., Li, H., Wang, X.: Learning feature pyramids for human pose estimation. In: ICCV, pp. 1281–1290 (2017)

    Google Scholar 

  27. Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: CVPR, pp. 6848–6856 (2018)

    Google Scholar 

  28. Howard, A., et al.: Searching for mobilenetv3. In: ICCV, pp. 1314–1324 (2019)

    Google Scholar 

  29. Andrew, H., Andrey, Z., Liang-Chieh, C., Mark, S., Menglong, Z.: Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation. In: CVPR, pp. 122–138 (2018)

    Google Scholar 

  30. Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: attention over convolution kernels. In: CVPR, pp. 11030–11039 (2020)

    Google Scholar 

  31. Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic ReLU. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12364, pp. 351–367. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58529-7_21

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Beijing Research Institute of UBTECH, Beijing, China

    Bin Sun

  2. Department of Automation, Center for Brain-Inspired Computing Research (CBICR), Tsinghua University, Beijing, China

    Mingguo Zhao

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  1. Bin Sun
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  2. Mingguo Zhao
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Correspondence to Mingguo Zhao .

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Editors and Affiliations

  1. MIMOS Berhad, Kuala Lumpur, Malaysia

    Duc Nghia Pham

  2. Sirindhorn International Institute of Science and Technology, Thammasat University, Mueang Pathum Thani, Thailand

    Thanaruk Theeramunkong

  3. Data61, CSIRO, Brisbane, QLD, Australia

    Guido Governatori

  4. Department of Philosophy, Tsinghua University, Beijing, China

    Fenrong Liu

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Sun, B., Zhao, M. (2021). Simple Light-Weight Network for Human Pose Estimation. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13033. Springer, Cham. https://doi.org/10.1007/978-3-030-89370-5_21

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  • DOI: https://doi.org/10.1007/978-3-030-89370-5_21

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  • Print ISBN: 978-3-030-89369-9

  • Online ISBN: 978-3-030-89370-5

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