无监督的域名适应人重新识别通过双层不对称的相互学习
IEEE transactions on neural networks and learning systems
|November 7, 2023
概括
这项研究引入了双层非对称的相互学习 (DAML),用于无监督域适应 (UDA) 人重新识别 (Re-ID). DAML通过使异质网络能够从不同的知识范围和嵌入空间中学习来提高行人识别的准确性.
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:
- 无监督域调整 (UDA) 人重新识别 (Re-ID) 地址识别跨不同数据集的行人,没有目标域标签.
- 现有的方法经常使用同质网络,限制知识多样性,导致共享错误.
- 这种限制阻碍了学习强大和歧视性表示的能力.
研究的目的:
- 提出一种新的双级不对称的相互学习 (DAML) 方法,以改进UDA人 Re-ID.
- 通过多样化的嵌入空间,从更广泛的知识范围进行学习.
- 克服现有方法中同质网络的局限性.
主要方法:
- DAML利用两个异质网络进行相互的知识学习.
- 不对称的子空间和硬蒸用于伪标签生成.
- 知识转移通过非对称的相互学习 (AML) 策略进行,教师网络适应目标领域,学生网络从基础真理标签中学习.
主要成果:
- 与最先进的方法相比,拟议的DAML方法显示出更高的性能.
- 在具有挑战性的公共数据集上进行了实验:Market-1501,CUHK-SYSU和MSMT17.
- 结果验证了从多样化的嵌入空间和不对称的知识转移中学习的有效性.
结论:
- 达姆尔有效地学习了对UDA人重新识别的歧视性表示.
- 非对称的相互学习方法通过利用各种知识来提高模型的稳定性.
- 该方法在跨域行人识别方面取得了重大进展.
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