基于多层一致性的弱监督的微观和宏观表达的发现
概括
本研究介绍了MC-WES,这是一种用于弱监督表达物发现 (WES) 的新框架,它使用多一致性机制从视频级标签中实现准确的级发现,克服现有方法的局限性.
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:
- 由于广泛的数据收集和级注释要求,在视频中发现微观和宏观表达是具有挑战性的.
- 现有的弱监督的表达发现 (WES) 方法,通常基于多个实例学习 (MIL),面临显著的跨模式,跨样本和跨任务差距.
- 样本间的差距,特别是关于样本分布和持续时间的差距,阻碍了当前WES方法的性能.
研究的目的:
- 提出一种新且简化的WES框架,MC-WES,旨在仅使用视频级标签实现细粒度的级发现.
- 通过减轻各种间隙问题和整合先前的知识来解决现有的WES方法的局限性.
- 开发一个系统,减轻框架智能注释的负担,同时保持高定位精度.
主要方法:
- MC-WES采用多一致性协作机制,包括模式级突出性,视频级分发,标签级持续时间和细分级特征一致性策略.
- 模态级突出一致性捕捉原始图像和光学流之间的相关性.
- 视频级分发一致性利用时间稀疏差异;标签级持续时间一致性利用面部肌肉持续时间变化;分段级特征一致性确保相同标签下的特征相似性.
主要成果:
- MC-WES仅使用视频级标签来展示有效的细级探测能力.
- 提出的多重一致性策略成功地弥补了现有的WES方法中发现的差距.
- 在CAS(ME) $^{2}$,CAS(ME) $^{3}$和SAMM-LV数据集上的实验结果显示,MC-WES的性能与最先进的完全监督的方法相美.
结论:
- MC-WES提供了一种简化但强大的方法来对弱监督的表达式发现,减少注释的复杂性.
- 多重一致性框架有效地合并了先前的知识,并解决了基于MIL的WES中固有的差距.
- MC-WES实现了竞争性性能,表明其可行性是作为对表达式发现任务完全监督方法的替代方案.
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