可靠的伪监督无监督域名自适应人员搜索的可靠伪监督
概括
本研究引入了无监督域适应 (UDA) 人员搜索的新框架,改善了新数据集上的模型性能. 在UDA人身搜索 (RPPS) 中可靠的伪监督框架增强了功能稳定性和代理可靠性,以获得更好的结果.
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) 人员搜索将模型调整为新的,未标记的数据集.
- 目前的方法由于光谱转移和静态代理更新而扎着不可靠的伪监督.
- 这些局限性阻碍了个人搜索模型的功能稳定性和适应性.
研究的目的:
- 开发一个新的框架,可靠的伪监督在UDA人搜索 (RPPS),以提高伪监督可靠性.
- 为了解决UDA人搜索中的光谱转移偏差和静态代理更新.
- 在跨领域场景中提高人搜索模型的稳定性和性能.
主要方法:
- 在UDA人身搜索 (RPPS) 框架中提出了可靠的伪监督.
- 引入了使用离散波段转换 (DWT) 进行特征分解和增强的双分支波段增强模块 (DWEM).
- 开发了一个动态信任加权集群代理 (DCCP),具有信任引导初始化和两阶段更新策略.
主要成果:
- 在CUHK-SYSU和PRW基准上,RPPS取得了最先进的表现.
- 对域名转移表现出强大的稳定性.
- 拟议的DWEM和DCCP模块显著提高了跨领域的稳定性和可区分性.
结论:
- 提高伪监督可靠性对于有效的UDA人员搜索至关重要.
- RPPS框架为跨领域的人员搜索挑战提供了强大的解决方案.
- 这些发现突显了波形变换和动态代理更新在域调整中的潜力.
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