矢量化证据学习用于弱监督的时间行动定位定位
IEEE transactions on pattern analysis and machine intelligence
|September 4, 2023
概括
矢量化证据学习 (VEL) 通过收集本地证据来提高模型性能来增强弱监督的时间动作本地化. 这种方法在视频中提供了更强大,更可靠的动作检测,即使有噪音数据或未知类别.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 模式分析 模式分析
背景情况:
- 弱监督的时间动作本地化 (WS-TAL) 对于视频分析至关重要,但面临的挑战来自弱监督和开放世界的场景.
- 现有的深度学习方法取得了进展,但由于固有的不确定性,它们在稳定性和可靠性方面扎.
研究的目的:
- 引入一个新的范式,向量化证据学习 (VEL),以改进WS-TAL.
- 在WS-TAL中应对不确定性,杂数据和未知类别的挑战.
主要方法:
- VEL使用可学习的元行动单元 (MAU) 作为行动类别的基本构建块.
- 它通过MAU和类别表示方式动态学习动作组件及其关系,并结合不确定性估计.
- 通过主体逻辑理论来积累和优化本地证据.
主要成果:
- 在正常,杂和开放设置中,VEL在强大可靠的动作本地化方面表现出了持续的改进.
- 在三项基准实验中,VEL与最先进的方法相比,证实了其优越的表现.
结论:
- 矢量化证据学习为WS-TAL提供了一个有希望的新方向.
- 拟议的方法有效地处理不确定性,并在具有挑战性的视频分析任务中提高本地化准确性.
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