Person Re-identification 系列论文笔记(一):Scalable Person Re-identification: A Benchmark
打算整理一个关于Person Re-identification的系列论文笔记,主要记录近年CNN快速发展中的部分有亮点和借鉴意义的论文。 论文笔记流程采用contributions->algorithm pipeline>experiments->个人评价 Scalable Pe...
Person Re-identification 系列论文笔记(二):A Discriminatively Learned CNN Embedding for Person Re-identification
A Discriminatively Learned CNN Embedding for Person Re-identification Zheng Z, Zheng L, Yang Y. A Discriminatively Learned CNN Embedding for Person Re...
Unsupervised Person Re-identification by Soft Multilabel Learning
简介: 这是一篇19年CVPR的跨域无监督Re-ID论文,在Market1501和DukeMTMC-reID上分别达到了67.7%和67.1%的rank-1精度,算是一篇将准确度刷得比较高的论文了,在这篇论文中主要是偏重了loss函数的设计而非网络结构,所以理解起来还是有一定难度的,下面就来一探它的...
论文笔记:Person Re-identification with Deep Similarity-Guided Graph Neural Network
Person Re-identification with Deep Similarity-Guided Graph Neural Network 2018-07-27 17:41:45 Paper: https://128.84.21.199/pdf/1807.09975.pdf 本文将 Gr...
Person Re-identification 系列论文笔记(三):Improving Person Re-identification by Attribute and Identity Learning
Improving Person Re-identification by Attribute and Identity Learning Lin Y, Zheng L, Zheng Z, et al. Improving Person Re-identification by Attribute ...
读论文系列:Deep transfer learning person re-identification
转载请注明作者:https://github.com/ahangchen arxiv 2016 by Mengyue Geng, Yaowei Wang, Tao Xiang, Yonghong Tian Transfer Learning 旧数据训练得到的分类器,在新的数据上重新训练,从而在...
【论文阅读】Batch Feature Erasing for Person Re-identification and Beyond
转载请注明出处:https://www.cnblogs.com/White-xzx/ 原文地址:https://arxiv.org/abs/1811.07130 如有不准确或错误的地方,欢迎交流~ 【作者的motivation】 https://zhuanlan.zhihu.com/p/5324...
Horizontal Pyramid Matching for Person Re-identification 论文笔记
Motivation Failure cases where the discriminative body parts (key parts) are missing. Contribution 1. horizontal pyramid scales (增强body parts...
论文笔记:Deep feature learning with relative distance comparison for person re-identification
这篇论文是要解决 person re-identification 的问题。所谓 person re-identification,指的是在不同的场景下识别同一个人(如下图所示)。这里的难点是,由于不同场景下的角度、背景亮度等等因素的差异,同一个人的图像变化非常大,因而不能使用一般的图像分类的方法。...