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双边关系蒸为弱监督的时间行动局部化

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    弱监督的时间动作定位 (WSTAL) 模型在准确的动作间隔识别方面扎. 我们的双边关系蒸 (BRD) 方法通过模拟类别和序列关系来改进WSTAL,以获得更好的结果.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 弱监督时间动作定位 (WSTAL) 使用视频级标签来检测未修剪的视频中的动作间隔.
    • 现有的WSTAL模型通常会产生不准确和不完整的本地化,因为它们专注于主导细分市场.

    研究的目的:

    • 提出一种新的方法,即双边关系蒸 (BRD),用于改进WSTAL.
    • 通过增强关系建模来解决当前WSTAL模型的局限性.

    主要方法:

    • 在类别和序列层面上,BRD共同建模关系.
    • 类别级别的关系是通过从预训练的语言模型中提取知识,使用相关性对齐和类别意识对比来捕获的.
    • 序列级关系是使用基于梯度的特征增强方法来建模的,以实现表示一致性.

    主要成果:

    • 拟议的BRD方法实现了最先进的性能.
    • 在THUMOS14和ActivityNet1.3数据集上进行了实验.

    结论:

    • 双边关系蒸 (BRD) 有效地改善了弱监督的时间动作定位.
    • 联合建模类别和序列关系对于视频中准确的动作本地化至关重要.