对抗性扰乱和防御对于可概括的人重新识别的防御
Hongchen Tan1, Kaiqiang Xu2, Pingping Tao3
1Institute of Future Technology, Dalian University of Technology, Dalian, Dalian 116024, China.
概括
本研究介绍了一种对抗性扰乱和防御 (APD) 方法,以改善域泛化人重新识别 (DG Re-ID). APD方法提高了硬匹配样本的特征分离,提高了 DG Re-ID 的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 域泛化人员重新识别 (DG Re-ID) 性能严重依赖于身份相关描述符的质量.
- 难以匹配的样本在GD Re-ID中构成了挑战,原因是难以将身份相关的特征与无关的特征分开,阻碍了概括.
研究的目的:
- 增强模型在硬匹配样本中区分身份相关特征和身份无关特征的能力,以改进GD Re-ID.
- 提出一种新的对抗性扰乱和防御 (APD) 重新识别方法,以实现高性能域泛化.
主要方法:
- 引入了一种使用度量对抗性来合成硬匹配样本的度量-扰生成网络 (MPG-Net),通过扰乱潜空间度量关系,同时保留视觉细节来合成硬匹配样本.
- 开发了一个语义净化网络 (SP-Net),在合成的硬匹配样本上进行训练,以捕获高质量的身份相关特征.
- 在SP-Net中整合了一个语义自我扰动和防御 (SSD) 计划,以解开和净化与身份相关的特征.
主要成果:
- 拟议的APD方法,利用MPG-Net和SP-Net与SSD,证明了在GD Re-ID任务中的有效性.
- 广泛的实验验证了APD方法能够改善硬匹配样本的特征分离的能力.
结论:
- APD 方法成功地提高了 DG Re-ID. 的硬匹配样本的特征解.
- 拟议的方法为在人重新识别系统中实现强大的域泛化提供了一个有希望的方向.
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