不确定性意识的双重证据学习对弱监督的时间行动定位进行定位
IEEE transactions on pattern analysis and machine intelligence
|August 25, 2023
概括
本研究引入了不确定性意识的双重证据学习 (UDEL),通过减少背景噪声来改善弱监督的时间动作定位 (WTAL). UDEL有效地将行动与背景区分开来,使用双不确定性来实现更好的定位准确性.
科学领域:
- 计算机视觉 计算机视觉
- 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 (WTAL) 旨在仅使用视频级标签识别动作实例和类别.
- 现有的WTAL方法因背景噪音和忽视非分别的行动段而扎在行动背景模糊性.
研究的目的:
- 提出一个新的框架,不确定性意识的双重证据学习 (UDEL),以解决WTAL中的行动背景模两可.
- 为了提高绩效,在一个证据深度学习 (EDL) 框架中利用认识系统和 aleatoric 不确定性.
主要方法:
- UDEL融合了视频层面的认识和定理不确定性,以量化背景噪声干扰.
- 断片级别的随机不确定性被推断为渐进的相互学习,以"轻而易举"的方式专注于行动实例.
- 该框架鼓励片段级的认识不确定性来补充前景注意力得分.
主要成果:
- 在四个公共基准上,UDEL取得了最先进的表现.
- 该方法有效地减少了行动背景的模糊性.
- 实验结果证明了拟议的以不确定性为导向的方法的有效性.
结论:
- UDEL提供了一个强大的解决方案,用于弱监督的时间动作定位.
- 整合双不确定性增强了模型处理杂和复杂视频数据的能力.
- 拟议的框架在行动本地化领域取得了重大进展.
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