方法概述

1，提出了一种用于无监督行人重识别的联合生成对比学习框架，生成和对比模块互相提高对方的性能。
2，在生成模块中，我们引入了3D网格生成器。
3，在对比模块，我们提出了一种视角无关的损失，来减少生成样本和原始样本之间的类内变化。

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
- 具体实现
实验结果
总体评价
引用格式
参考文献

内容概要

论文名称	简称	会议/期刊	出版年份	baseline	backbone	数据集
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification	GCL	CVPR	2021	【JVTC】Li, J., Zhang, S.: Joint visual and temporal consistency for unsupervised domain adaptive person re- identification. pp. 1–14 (2020)	ImageNet [32] pre-trained ResNet50 [17] with slight modifications	Market-1501、DukeMTMC-reID, MSMT17 [41]

在线链接：https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Joint_Generative_and_Contrastive_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.html
源码链接： https: //github.com/chenhao2345/GCL.

工作概述

1, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework.
2, While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant fea- tures for generation.
3, we propose a mesh- based view generator. Specifically, mesh projections serve as references towards generating novel views of a per- son.
4,we propose a view-invariant loss to fa- cilitate contrastive learning between original and gener- ated views.

成果概述

our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID dat- sets.

方法详解

方法框架

Figure 2: (a) General architecture of GCL: Generative and contrastive modules are coupled by the shared identity encoder Eid. (b) Generative module: The decoder G combines the identity features encoded by Eid and structure features Estr to generate a novel view x′
new with a cycle consistency. © Contrastive module: View-invariance is enhanced by maximizing the agreement between original Eid(x), synthesized Eid(x′
new) and memory fpos representations.

Figure 3: Example images as generated by the View Generator via 3D mesh rotation based on left input image.

具体实现

1，GCL框架主要包含了生成模块和对比模块两个模块。
2，在生成模块中，文章通过HMR构建3D网格，提取图像的外观和姿势。然后通过对姿势进行不同角度的旋转来重新构成样本，以此从样本、特征和解码结果三个层面构成损失gan。

3，在对比模块中，文章维护了一个内存条（memory bank）来存储样本的特征向量，并在迭代过程中根据公式5更新。然后从前面诸多的样本中构造正负样本对，然后求对比损失。

4，联合训练采用热启动的形式，基于baseline工作训练先进行40epoch学习gan损失，在最后20个epoch才学习总体损失（公式9）

实验结果

总体评价

1, 基本上所有创新点都基于一开始想到了是用3D网格来生成样本，在这个基础上，后面的创新点都水到渠成的出来了。
2，感觉各种样本的合成以及组合有点繁杂了。
3，当没有一个漂亮的大图的时候，多部分组图也可以成为framework。画图不够高端。

小样本学习与智能前沿（下方↓公众号）后台回复[JVTC+*]，即可获得论文电子资源。

引用格式

@inproceedings{DBLP:conf/cvpr/ChenWLDB21,
author = {Hao Chen and
Yaohui Wang and
Benoit Lagadec and
Antitza Dantcheva and
Fran{\c{c}}ois Br{’{e}}mond},
title = {Joint Generative and Contrastive Learning for Unsupervised Person
Re-Identification},
booktitle = {{CVPR}},
pages = {2004–2013},
publisher = {Computer Vision Foundation / {IEEE}},
year = {2021}
}

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