学习概率的存在-缺席证据对弱监督的视听事件感知
概括
本研究引入了一种用于弱监督视听事件感知 (WS-AVEP) 的新方法,使用概率存在缺席证据 (PPAE) 和联合模态相互学习 (JML) 改进事件本地化和分类. 这种方法提高了音频和视觉指标的性能.
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:
- 弱监督的视听事件感知 (WS-AVEP) 面临着由于视听数据不同步和监督较弱的挑战.
- 现有的方法往往忽视了事件检测的音频和视觉模式的互补性质.
研究的目的:
- 为WS-AVEP开发一个统一的框架,有效地利用来自互补模式的存在和缺席证据.
- 解决在弱监督下从复杂的视听数据中学习可靠证据的不确定性.
主要方法:
- 提出了一个概率存在缺席证据 (PPAE) 收集器,利用单模和跨模表示.
- 引入了一种联合模式的相互学习 (JML) 过程,以适应性和动态性地校准证据.
- 设计了一个处理可听,可视和视听事件的系统.
主要成果:
- 提出的方法比最先进的方法取得了显著的改进.
- 在LLP数据集上,事件级音频和视觉指标的绝对收益分别为3.1%和4.2%.
结论:
- 通过利用互补的模式信息,PPAE框架和JML过程有效地增强了WS-AVEP.
- 该方法提供了一个强大的解决方案,可以暂时定位和分类监督较弱的事件.
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