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2164 软件学报 2025 年第 36 卷第 5 期

型构造了一个以 DDA-STGConv 为基本单元的平行多尺度网络, 通过构造跨域邻接矩阵同时捕捉空域和时域的动
态判别特征, 利用注意力机制探索节点之间的关联关系, 增强节点间的信息交互. 此外, 通过设计图拓扑聚合函数,
构造基于不同图拓扑结构的平行多尺度子网络模块, 实现不同尺度节点特征的聚合, 有效提取骨架关节点局部和
全局的特征信息. 同时, 设计多尺度特征交叉融合模块 (MFEB), 加强平行网络间多尺度信息的交互, 增强网络模
型特征表示的能力. 在两大人体姿态估计数据集上与目前主流的方法进行对比, 实验结果表明所提出的网络模型
获得较好的姿态估计结果. PMST-GNet 模型的灵活性可为行为识别, 运动预测及场景理解等领域的工作提供技术
支持.

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