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Greit-HRNet: Grouped Lightweight High-Resolution Network for Human Pose Estimation

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Computer Vision – ACCV 2024 (ACCV 2024)

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Abstract

As multi-scale features are necessary for human pose estimation tasks, high-resolution networks are widely applied. To improve efficiency, lightweight modules are proposed to replace costly point-wise convolutions in high-resolution networks, including channel weighting and spatial weighting methods. However, they fail to maintain the consistency of weights and capture global spatial information. To address these problems, we present a Grouped lightweight High-Resolution Network (Greit-HRNet), in which we propose a Greit block including a group method Grouped Channel Weighting (GCW) and a spatial weighting method Global Spatial Weighting (GSW). GCW modules group conditional channel weighting to make weights stable and maintain the high-resolution features with the deepening of the network, while GSW modules effectively extract global spatial information and exchange information across channels. In addition, we apply the Large Kernel Attention (LKA) method to improve the whole efficiency of our Greit-HRNet. Our experiments on both MS-COCO and MPII human pose estimation datasets demonstrate the superior performance of our Greit-HRNet, outperforming other state-of-the-art lightweight networks.

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Acknowledgments

This study was funded by Professor Yingxia Yu. We would like to express our sincere gratitude to Professor Yanxia Wang for her invaluable guidance throughout this research. We also extend our heartfelt thanks to Shuyao Shang for his assistance and support.

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Authors and Affiliations

  1. The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China

    Junjia Han

  2. Chongqing Normal University, Chongqing, 400047, China

    Yanxia Wang

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  1. Junjia Han
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  2. Yanxia Wang
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Correspondence to Junjia Han .

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Editors and Affiliations

  1. Pohang University of Science and Technology (POSTECH), Pohang, Korea (Republic of)

    Minsu Cho

  2. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Ivan Laptev

  3. Google, Mountain View, CA, USA

    Du Tran

  4. National University of Singapore, Singapore, Singapore

    Angela Yao

  5. Peking University, Beijing, China

    Hongbin Zha

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Han, J., Wang, Y. (2025). Greit-HRNet: Grouped Lightweight High-Resolution Network for Human Pose Estimation. In: Cho, M., Laptev, I., Tran, D., Yao, A., Zha, H. (eds) Computer Vision – ACCV 2024. ACCV 2024. Lecture Notes in Computer Science, vol 15472. Springer, Singapore. https://doi.org/10.1007/978-981-96-0885-0_15

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  • DOI: https://doi.org/10.1007/978-981-96-0885-0_15

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