背景知识
内容概要
针对的问题 or 出发点： 人工标注成本大。 还是为了解决小样本问题。
提出了渐进的多任务网络 （PMT-net）: 用one-shot初始化模型，然后迭代优化（秉承EUG一脉）
- 首先，行人的属性识别作为辅助任务。 （参照了APR那篇文章，也是EUG同作者的文章）
- 基于学到的特征，根据特征空间中的距离估计身份标签。
- 此外，为了提高对未标记样本的标签估计的准确性，设计了一种半监督聚类方法——距离加权聚类(Distance Ranked Weight clustering, DRW-Clustering)。 聚类方法根据距离排序的索引顺序对部分未标记样本进行加权，从而能够快速有效地找到真实的聚类中心。
实验结果表明，所提出的方法在one-shot person reid 中达到了与现有方法相当或更好的性能。
相关工作
- Person RE-ID method
- Multi-task learning
- Progressive algorithms
- Semi-supervised clustering
数据集
- Market1501
- DukeMTMC-reID
方法提要
方法框架
实验结果
方法详解
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