软弱和单框监督时间句子与高斯基于对比的命题学习接地
IEEE transactions on pattern analysis and machine intelligence
|December 17, 2025
概括
相反的提议学习 (CPL) 通过在视频中生成更好的提议来改善弱监督的时间句子接地. 这种方法有效地区分出令人困惑的场景,并为最先进的性能平衡注释成本.
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:
- 时间句子接地根据语言查询定位视频时刻.
- 由于时间边界注释的困难,弱监督的方法正在获得引力.
- 现有的方法使用低质量,内容独立的提案,并忽略视频内负面样本.
研究的目的:
- 引入对比性命题学习 (CPL) 以改善弱监督的时间句子接地.
- 解决现有方法的局限性,包括提案质量和负样本选择.
- 探索注释努力和接地性能之间的平衡.
主要方法:
- 在同一个视频中使用可学习的不对称高斯函数生成正负的命题.
- 实施可控,易于硬化的消极提案挖掘策略,以识别混的细分市场.
- 扩大低成本单框注释的提案生成.
主要成果:
- CPL有效地区分了高度混的视频段.
- 该方法在基准数据集 (Charades-STA,ActivityNet Captions,DiDeMo) 上实现了最先进的性能.
- 该方法证明了对基于MIL和基于重建的框架的适用性.
结论:
- 对比性命题学习提供了一种优越的方法,用于弱监督的时间句子接地.
- 拟议的方法增强了提案生成和负采样策略.
- CPL提供了一个灵活的解决方案,可以适应不同的注释负担,并实现顶级结果.
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