深度学习re-id综述

特征提取

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global feature

借助人体图像分割

Human Semantic Parsing for Person Re-identification CVPR2018
把人体图像分割成几个部分做attention
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多尺寸

Multi-scale Deep Learning Architectures for Person Re-identification ICCV2017
手工设计多尺度的inception流
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Attention

Harmonious AttentionCNN(HA-CNN)CVPR2018
软注意力(channel+spatial)+硬注意力（指导local brach）

local feature

人体部分/区域特征的集合，解决对齐问题，身体部分由人体姿态构建或者粗略的垂直分割完成

Auxilary 辅助方法

需要额外的标注信息

语意属性标注
view
View Confusion Feature Learning for Person Re-identification
ICCV2019
通过分类器得到view信息，然后通过分类器对抗损失、视觉feature损失、sift损失迫使提取出viewpoint invariant的feature
camera
data augumentation

位姿约束

guild 指导符合原来的分类
相机风格信息

camstyle
将外观和结构分离生成图片
无监督域适应

SPGAN eSPGAN

Person Transfer GAN to Bridge Domain Gap for Person Re-Identification- PTGAN
Wei et al. [35] handle the domain
gap problem by proposing a Person Transfer Generative
Adversarial Network (PTGAN), transferring the knowledge
from one labeled source dataset to another unlabeled target
dataset.
An image-image domain adaptation method [118]
is developed with preserved self-similarity and domaindissimilarity, trained with a similarity preserving generative adversarial network (SPGAN).
A Hetero-Homogeneous Learning (HHL) method [215] simultaneously considers the
camera invariance with homogeneous learning (image pairs
from the same domain) and domain connectedness with
heterogeneous learning (cross-domain negative pairs).