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FaSRnet: a feature and semantics refinement network for human pose estimation
Regular Papers | Updated：2024-04-29

    • FaSRnet: a feature and semantics refinement network for human pose estimation

    • FaSRnet：用于人体姿态估计的特征和语义修正网络
    • “多帧人体姿态估计领域取得了重要突破。针对运动模糊、视频失焦和遮挡等挑战，研究人员提出了一种新的人体姿态估计框架，通过特征层面和语义层面的精细化处理，有效提高了姿态估计的准确度。该框架利用连续帧之间的时间一致性，将辅助特征与当前帧特征进行对齐，减少了不同特征分布带来的损失。同时，引入注意力机制，实现了辅助特征与当前特征的融合。在语义层面，利用相邻热图之间的差异信息作为辅助特征，对当前热图进行精细化处理。实验表明，该方法在PoseTrack2017和PoseTrack2018等大型基准数据集上取得了显著成果，验证了其有效性。这一研究不仅为多帧人体姿态估计领域提供了新的解决方案，也为相关领域的深入研究奠定了坚实基础。”
    • Frontiers of Information Technology & Electronic Engineering   Vol. 25, Issue 4, Pages: 513-526(2024)

    • DOI：10.1631/FITEE.2200639    

      CLC： TP391

    • Received：12 December 2022，

      Revised：2023-06-27，

      Accepted：27 June 2023，

      Published：0 April 2024

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  • Yuanhong ZHONG, Qianfeng XU, Daidi ZHONG, et al. FaSRnet: a feature and semantics refinement network for human pose estimation[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(4): 513-526. DOI： 10.1631/FITEE.2200639.

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