一个多层次的关系意识变压器模型用于被封闭的人重新识别和重新识别
Guorong Lin1, Zhiqiang Bao2, Zhenhua Huang3
1School of Artificial Intelligence, South China Normal University, Foshan 528225, China.
概括
这项研究引入了一种新的多级关系意识转换器 (MLRAT) 模型,通过学习图像补丁和样本之间的关系来改进被封闭的人重新识别 (Re-ID),优于对被封闭数据集的现有方法.
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:
- 被遮蔽的人重新识别 (Re-ID) 是一个重大的挑战,因为对象或其他行人遮蔽.
- 现有的Re-ID方法通常依赖于像姿势估计这样的辅助模型,这种模型在闭塞时可能不可靠.
- 以前的方法经常从单个图像中学习特征,忽视了对强大的Re-ID至关重要的样本间关系.
研究的目的:
- 开发一种新的多级关系意识变压器 (MLRAT) 模型,用于增强被封闭的人重新识别.
- 解决现有Re-ID技术中辅助模型和单图像特征学习的局限性.
- 为了提高人重识别的准确性和稳定性,在具有显著遮的场景中.
主要方法:
- 引入了多级关系感知变压器 (MLRAT) 模型,包括补丁级关系感知 (PLRA) 和样本级关系感知 (SLRA) 模块.
- PLRA使用图形卷积网络 (GCN) 来模拟关键图像补丁之间的结构关系,绕过辅助模型.
- SLRA使用关系意识变压器 (RAT) 块和自蒸来建模样本间的关系和转移知识.
主要成果:
- 在四个被封闭的人 Re-ID 数据集上,MLRAT 模型显示了与现有基线相比显著的性能改进.
- 该模型在部分和整体人重识别数据集上保持了竞争性表现.
- 通过模拟补丁关系,PLRA有效地学习了局部特征,而SLRA捕获了歧视性样本级特征.
结论:
- 拟议的MLRAT模型通过利用多层次的关系建模,有效地解决了被封闭的人重新识别的挑战.
- 新的PLRA和SLRA模块为学习歧视性特征提供了强大的框架,而不依赖辅助模型.
- MLRAT为被封闭的人重新识别提供了优质的解决方案,在各种数据集类型中显示出强大的概括性.
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