道语义相互学习可见热人重新识别
Yingjie Zhu1,2, Wenzhong Yang2
1College of Software, Xinjiang University, Urumqi, China.
PloS one
|January 19, 2024
概括
这项研究引入了一个新的道语义互学习网络 (CSMN),以应对可见红外人重新识别 (VI-ReID) 的挑战. 通过优化道层面的语义一致性,CSMN增强了跨模式匹配,在基准数据集上实现了卓越的性能.
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:
- 可见红外人重新识别 (VI-ReID) 对于监控和安全至关重要.
- 可见和红外图像之间的模式差异是一个重大挑战.
- 现有的方法经常使用单独的网络,限制跨模式特征学习.
研究的目的:
- 为VI-ReID提出一个新的道语义相互学习网络 (CSMN).
- 通过专注于道级语义差异来解决模式差异.
- 为了提高不同成像模式的行人匹配的准确性和稳定性.
主要方法:
- 开发了CSMN,这是一个优化从本地和全球视角道之间的语义一致性的网络.
- 引入了通道级自动引导双度量损失 (CADM),用于细粒度学习模态不变特征.
- 采用双优化策略来增强语义对齐.
主要成果:
- 在RegDB和SYSU-MM01数据集上,CSMN表现出卓越的性能.
- 在RegDB数据集上获得了3.43%的排名-1得分和0.5%的mINP得分的改善.
- 验证了道级语义相互学习和CADM损失的有效性.
结论:
- 拟议的CSMN有效地解决了VI-ReID中的模式差异.
- 道级语义互学习为跨模式检索提供了一个有希望的方向.
- CSMN为VI-ReID任务设定了一个新的最先进的绩效基准.
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