方法概述

1，提出 cluster contrast（聚类对比）来存储特征向量和计算对比损失。
2，展示了通过聚类级别的内存字典，可以解决聚类特征表达不一致的问题。

文章目录

方法概述
内容概要
- 工作概述
- 成果概述
方法详解
- 方法框架
- 算法描述
- 具体实现
实验结果
总体评价
引用格式
参考文献

小样本学习与智能前沿（下方↓公众号）后台回复“CCU”，即可获得论文电子资源。

内容概要

论文名称	简称	会议/期刊	出版年份	baseline	backbone	数据集
Cluster Contrast for Unsupervised Person Re-Identification	CCU	arxiv	2021	【SpCL】Yixiao Ge, Feng Zhu, Dapeng Chen, Rui Zhao, and hong- sheng Li. Self-paced contrastive learning with hybrid mem- ory for domain adaptive object re-id.	ImageNet-pretrained [7] ResNet-50 [18]、use DBSCAN [9] for clustering	Market- 1501 [52], DukeMTMC-reID [32], MSMT17 [42], Per- sonX [35], and VeRi-776 [26]

在线链接：https://arxiv.org/pdf/2103.11568.pdf
源码链接： https://github.com/alibaba/cluster-contrast-reid

工作概述

1，we present Cluster Contrast which stores feature vectors and computes contrast loss in the cluster level.
2，We demonstrate that the inconsistency problem for cluster feature represen- tation can be solved by the cluster-level memory dictionary

成果概述

By straightforwardly applying Cluster Contrast to a stan- dard unsupervised re-ID pipeline, it achieves considerable improvements of 9.5%, 7.5%, 6.6% compared to state-of- the-art purely unsupervised re-ID methods and 5.1%, 4.0%, 6.5% mAP compared to the state-of-the-art unsupervised domain adaptation re-ID methods on the Market, Duke, and MSMT17 datasets.

方法详解

方法框架

Figure 2: The unsupervised person re-ID pipeline. Feature vectors with the same color belong to the same cluster. The upper part is the memory initialization stage. Training data features are assigned pseudo labels by clustering algorithm. The lower part is the model training stage. Hard exampling method is used to select the hard query instance to update memory feature. The ClusterNCE loss computer contrastive loss between query features and all cluster features.

Figure 3: The comparison to existing memory based non-parametric classification loss.

算法描述

具体实现

1，采用ImageNet预训练过的 resnet 50 ，采用 DBSCAN 为聚类方法。
2，对于内存的初始化。从聚类样本中，随机选择一个样本特征作为聚类的特征。

2，内存的更新，选择和原有聚类特征差别最大的样本特征，和现有聚类特征进行加权求和。

3，损失函数。求查询样本和聚类特征之间的loss。

实验结果

总体评价

1，在Spcl的基础上，从UDA转为了 USL，在整体的训练loss上，就没有源域部分。另外，内存也不载叫混合内存了，而是单一的存储聚类特征。
2，最有创新性的点在于，一是内存中聚类特征的表示，初始化的时候采用的是随机采样，更新过程中，采用的是hardest sample。文章的discussion部分也对这两个机制为何work，做出了解释。
3，总体来说，方法清晰简单，虽然只有三个公式，但最终的实验结果表明，还是很work的。
4.，不过感觉文章写得不那么能够突出文章的contribution。

引用格式

@article{DBLP:journals/corr/abs-2103-11568,
author = {Zuozhuo Dai and
Guangyuan Wang and
Siyu Zhu and
Weihao Yuan and
Ping Tan},
title = {Cluster Contrast for Unsupervised Person Re-Identification},
journal = {CoRR},
volume = {abs/2103.11568},
year = {2021}
}

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