弱监督的Tracklet协会学习与视频标签用于个人重新识别
IEEE transactions on pattern analysis and machine intelligence
|December 22, 2023
概括
本研究引入了一个弱监督的轨道联想学习 (WS-TAL) 模型,用于人重新识别 (re-id). 这种新的方法有效地降低了标签成本,同时实现了高精度,优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 被监督人员重新识别 (re-id) 需要广泛的手动标签.
- 与监督替代品相比,无监督的再贷款方法的性能较低.
- 现有的监管较弱的方法通常需要辅助数据集或与噪音数据作斗争.
研究的目的:
- 开发一种新的弱监督人员重新识别模型,仅使用视频标签.
- 解决现有的弱监管方法的局限性,特别是它们依赖辅助数据和处理杂的轨道.
主要方法:
- 制定了一个弱监督的轨道联想学习 (WS-TAL) 模型.
- 引入了一种用于伪标签和特征学习的袋内轨道区分学习 (ITDL) 术语.
- 开发了一种跨袋轨道联结学习 (CTAL) 术语,用于挖掘袋间的轨道联结.
- 共同优化ITDL和CTAL的模式培训术语.
主要成果:
- 在MARS数据集上获得了88.1%的排名-1准确性.
- 在DukeMTMC-VideoReID数据集上实现了90.3%的排名-1准确性.
- 显著超过了最先进的弱监管模型,甚至一些完全监管的再贷款模型.
结论:
- 该WS-TAL模型有效地学习使用仅视频标签的歧视性特征和关联.
- 这种方法为人重新识别提供了一个实用且高性能的解决方案,减少了标签的努力.
- 与现有的弱监督和一些监督的反技术相比,拟议的方法显示出更高的性能.
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