对比式学习增强了伪标签,用于无监督的域名适应,人体重新识别
Xuemei Bai1, Yuqing Zhang1, Chenjie Zhang1
1School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, Jinlin, China.
PloS one
|July 14, 2025
概括
本研究引入了一种使用无监督域调整进行人重新识别 (ReID) 的新方法. 它精制伪标签以减少噪音,显著提高监控应用中的模型性能.
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:
- 个人重新识别 (ReID) 对于智能监控和公共安全至关重要.
- 域差异对ReID模型的概括提出了挑战.
- 无监督域名适应 (UDA) 减少了对标记数据的依赖,但受到了杂的伪标签的影响.
研究的目的:
- 改进个人ReID的无监督域调整.
- 为了减轻伪标签噪音对模型培训的影响.
- 提高ReID模型在目标领域的概括能力.
主要方法:
- 采用对比学习来增强目标域特征表示和歧视.
- 引入了用于伪标签改进的新浪损失功能,减少标签噪声干扰.
- 在Person ReID.中适应无监督域调整的方法.
主要成果:
- 在Market1501上实现了91.4%的排名-1准确度,在DukeMTMC上达到81.4%.
- 在Market1501上获得了79.0%的mAP,在DukeMTMC上获得了67.9%.
- 在目标域上显著改善了ReID性能.
结论:
- 拟议的基于对比学习的伪标签改进有效地解决了Person ReID的UDA中的标签噪声.
- 该方法提高了跨域迁移性能和模型通用性.
- 为面临域名转移和噪音标签的Person ReID任务提供有效的技术支持.
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