欧几里德-距离-保存特征减少,以有效地重新识别人
Guan'an Wang1, Xiaowen Huang2, Yang Yang3
1School of Electronic and Computer Engineering, Peking University, China.
概括
本研究引入了欧几里德距离维护特征减少 (EDPFR) 以有效地减少人重新识别 (Re-ID) 深度学习模型中的特征维度. EDPFR通过保持欧几里德距离来保持准确性,并增强知识蒸以提高性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 人重识别 (Re-ID) 使用深度神经网络在相机上匹配人形图像.
- 当前的Re-ID方法创造了高维特征,增加了计算和存储的复杂性.
- 现有的特征减少技术在端到端优化或理论保证方面存在局限性.
研究的目的:
- 提出一种新的方法,即欧几里德距离维护特征减少 (EDPFR),以解决Re-ID.中的高维特征的复杂性.
- 将训练后减少和训练期间减少方法的优势结合起来,以改进Re-ID.
- 引入特征级别的蒸损失,以实现更灵活,更有效的知识传输.
主要方法:
- EDPFR将特征缩减表达为矩阵分解,条件是保持欧几里德距离.
- 矩阵分解集成到深度神经网络中,用于端到端优化和批量训练.
- 提出了一种新的特征级别蒸损失,利用欧几里德距离保存属性进行直接的特征空间蒸.
主要成果:
- EDPFR有效地减少了特征尺寸,同时保持了特征之间的欧几里德距离 (L2{\displaystyle fa,fb}=L2{\displaystyle fa,fb}).
- 拟议的方法通过直接在功能空间中进行知识蒸来提高知识蒸的灵活性和效率.
- 对Market-1501,DukeMTMC-reID和MSMT数据集的实验证明了EDPFR的有效性.
结论:
- EDPFR提供了一个理论上合理且实际上高效的解决方案,用于减少个人重新识别中的特征尺寸.
- 通过将EDPFR集成到深度网络中,可以实现强大且优化的Re-ID模型.
- 新的特征级蒸增强了知识蒸在Re-ID任务中的应用性和性能.
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