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具有多个动作元件的片段识别变压器,用于基于骨架的动作细分.

Haoyu Ji, Bowen Chen, Wenze Huang

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    概括
    此摘要是机器生成的。

    基于骨架的时间行动细分 (STAS) 方法在识别关键行动元素方面遇到了困难. 拟议的具有多个动作元素 (ME-ST) 的片段感知变压器显著提高了动作歧视和细分精度.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 人类运动分析分析

    背景情况:

    • 基于骨的时间动作细分 (STAS) 对于理解未经修剪的骨运动序列中的人类行为至关重要.
    • 使用图形卷积网络 (GCN) 和时间卷积网络 (TCN) 的现有方法往往无法捕捉核心身体部位和子动作等基本的动作元素,从而限制了性能.
    • 这种限制阻碍了在一个序列中的不同动作之间的准确区分.

    研究的目的:

    • 引入一种新的方法,即具有多个动作元素 (ME-ST) 的片段感知变压器,以增强STAS中的动作歧视和细分.
    • 利用短暂的注意力机制来识别各种规模的核心关节和关键子行动,解决先前方法的局限性.

    主要方法:

    • ME-ST 模型利用 intrasnippet 交叉关节注意力 (CJA) 进行空间建模,通过在片段内建立关节之间的语义关系来识别核心运动关节.
    • 对于时间建模,它在编码器中采用了intrasnippet跨注意力 (CFA) 来突出辨别,并在解码器中采用了带有intrasnippet跨尺度注意力 (CSA) 的沙钟样本,以集成多尺度的时间信息.

    主要成果:

    • 与现有方法相比,ME-ST模型在行动歧视和细分方面表现优越.
    • 对五个公共数据集的评估证实了拟议的ME-ST方法的最先进的 (SOTA) 有效性.
    • 该模型成功地识别并利用关键行动要素,提高了细分精度.

    结论:

    • ME-ST模型有效地解决了以前的STAS方法的局限性,通过专注于基本的行动元素.
    • 在片段中提出的注意力机制增强了模型准确识别和细分行动的能力.
    • ME-ST代表了基于骨架的时间动作细分的重大进步,实现了SOTA的结果.