对于时空视频接地的单监控
概括
本研究介绍了一种具有成本效益的空间时视频接地 (STVG) 方法,使用单监控. T-SMILE方法显著提高了比现有的监管较弱的技术更准确的准确性.
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:
- 空间时空视频接地 (STVG) 使用自然语言查询定位视频中的对象.
- 目前对STVG的监督较弱的方法由于劳动密集型管注释而遭受性能下降.
研究的目的:
- 开发一种使用单监控,具有可接受的准确性,更便宜的STVG方法.
- 解决监管较弱的STVG的绩效差距.
主要方法:
- 拟议的双阶段多个实例学习 (T-SMILE) 方法.
- 使用单边界框注释来创建用于训练的伪标签.
- 包含用于时间识别的多个实例学习,用于特征学习的空间先决约束,以及用于分支适应的课程学习.
主要成果:
- T-SMILE显著优于现有的监管较弱的STVG方法.
- 尽管注释成本较低,但实现的性能与一些完全监督的方法相比或更好.
- 引入了对单框注释STVG的大型基准.
结论:
- 单框架监管是有效的STVG的可行范式.
- T-SMILE为准确且具有成本效益的STVG提供了一个有前途的解决方案.
- 新的基准将促进对监管较弱的STVG的未来研究.
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