Improving Person Re-identification by Attribute and Identity Learning 泛读记录

最新推荐文章于 2022-05-19 14:12:51 发布
最新推荐文章于 2022-05-19 14:12:51 发布
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本文链接：https://blog.youkuaiyun.com/CsdnWujinming/article/details/90758065
本文推荐一篇关于改进行人重识别技术的论文，通过引入属性检测器辅助重识别器，提出一种包含属性检测的多任务网络。该方法在两个大数据集上验证了有效性，通过身份识别损失和属性分类损失的结合，实现更精准的人脸识别。
【论文笔记】Improving Person Re-identification by Attribute and Identity Learning
Chen_yuazzy的博客
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Improving Person Re-identification by Attribute and Identity Learning 论文链接 1.摘要&介绍 (1)Re-ID：全局特征，Attribute recognition：局部特征。将这两个方面融合形成新的网络APR，在CNN的基础结构上改进。 (2)Person Re-ID是在非重叠相机下找到查询行人；Attrib...
论文阅读之 Improving person re-identification by attribute and identity learning
qq_39220334的博客
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提示：文章写完后，目录可以自动生成，如何生成可参考右边的帮助文档 文章目录 前言 一、pandas是什么？ 二、使用步骤 1.引入库 2.读入数据 总结 前言 提示：这里可以添加本文要记录的大概内容： 例如：随着人工智能的不断发展，机器学习这门技术也越来越重要，很多人都开启了学习机器学习，本文就介绍了机器学习的基础内容。 提示：以下是本篇文章正文内容，下面案例可供参考 1. 摘要 提出问题：行人重识别和属性识别任务都需要学习**行人描述**，不同之处在描述**...
从多任务角度看《Improving Person Re-identification by Attribute and Identity Learning》
zzccxx5的博客
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本人重点做的是多任务学习，但是这方面资料有限，进展缓慢，平时做的是人脸属性分析，在查找文献时，偶然看到这篇论文，感觉看起来思想还可以~因为对行人重识别领域没什么了解，所以只从多任务角度来重点记录下这篇论文。 首先提下这篇文章作者，是一位在UTS在读的博三学生，发过好几篇顶会文章，羡煞我也，主页在此，有很多行人重识别方向的研究，基本都有代码，唯独这篇没有，有点难过！ 下面进入正文，相...
Improving Person Re-identification by Attribute and Identity Learning
qq_35091353的博客
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这篇文章是关于利用行人属性提升行人再识别 论文链接...
paper reading——《Improving Person Re-identification by Attribute and Identity Learning》
啊啊啊
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阅读翻译论文：Improving Person Re-identification by Attribute and Identity learning
Salience-Guided Cascaded Suppression Network for Person Re-identification
soarflight_ing的博客
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Salience-Guided Cascaded Suppression Network for Person Re-identification 论文：Salience-Guided Cascaded Suppression Network for Person Re-identification，CVPR,2020. 链接:paper 摘要 动机： 使用注意力机制来建模全局和局部特征作为最终的行人表示已经成为人再识别(Re-ID)算法的趋势。这些方法的一个潜在限制是它们集中于最显著的特征，但是对一个
泛读论文：Person-reID 行人重识别合集
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汪汪汪的博客
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2017CVPR、ICCV和NIPS在Person Reidentification方向的相关工作小结 基于融合特征的行人再识别方法 模式识别与人工智能 2017.3 问题 目前常用的行人再识别方法主要集中在行人外形特征的描述和同一行人对应的 2 幅图像之间距离的学习度量．由于行人图像的亮度和相机角度的变化等，提取行人的外形特征的不变性较难，因此在各个图像库上行人再识别的识别率较低 ...
Improving person re-identification by attribute and identity learning[reading notes]
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论文连接 Improving person re-identification by attribute and identity learningAbstract研究现状属性在文本中attribute-person recognition (APR) network (属性-人物识别网络)研究者做了什么实验结果Introduction属性在本文中本文的动机属性标签的有效性是三倍的相关研究和以前工作...
【论文阅读】Improving Person Re-identification by Attribute and Identity Learning
Yolo的博客
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这篇文章是由悉尼科技大学Yutian Lin等人完成，在CVPR2016上被收录。论文地址：https://arxiv.org/pdf/1703.07220.pdf 文章主要利用了属性学习（attribute learning）和行人重识别（Re-ID）之间的关系，将在局部特征上表现很好的属性学习和全局学习中的行人重识别方法进行结合，从而构造出一个全新的损失函数，进而提高Re-ID的性能。 文...
【论文阅读-5】Improving Person Re-identification by Attribute and Identity Learning（ 通过属性和身份学习来提高ReId）
weixin_42040467的博客
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Abstract 1.现状：只考虑了行人的标识标签； 2.本文：发现了包含详细的局部描述的属性有助于reid模型学习更有区别的特征表示； 3.提出：基于属性标签和ID标签的互补性，提出了一种属性行人重识别（APR）网络； 4.APR：一种学习reid嵌入，同时预测行人属性的多任务网络。 5.另外：考虑到了属性之间的依赖性和相关性，重新加权了属性预测。 1. Introduction 1.ReId是一项从非重叠的摄像机中寻找被查询者的任务；属性识别的目标是从图像中预测一组属性的存在；属性描述了一个人的详
Person Re-identification 系列论文笔记（三）：Improving Person Re-identification by Attribute and Identity Lear...
weixin_30852367的博客
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Improving Person Re-identification by Attribute and Identity Learning　　 Lin Y, Zheng L, Zheng Z, et al. Improving Person Re-identification by Attribute and Identity Learning[J]. 2017. 　　这篇论文主要是将attr...
《Improving Person Re-identification by Attribute and Identity Learning》论文笔记
weixin_42643931的博客
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《Improving Person Re-identification by Attribute and Identity Learning》论文笔记MotivationContributionsMethodExperiment参数设置Person Re-ID评估属性识别评估不同属性对ReID的影响 作者：Yutian Lin, Liang Zheng, Zhedong Zheng, Yu Wu ...
Multi-Task Learning with Low Rank Attribute Embedding for Person Re-identification
cv_family_z的博客
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ICCV 2015http://www.umiacs.umd.edu/~fyang/mtl-lorae.htmlPerson Re-identification 方面的文献，本文主要引入了 person attributes correlation 即人的一些属性具有相关性，利用这些相关性来进行人的检索。Our contributions： 1）针对 Person Re-identificatio
Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification阅读总结
qq_35226955的博客
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Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification（TJAIDL）阅读笔记 这篇文章时第一次在无监督跨域reid之中引入属性的paper。 最经典的属性reid的文章当然是ZL老师组的：Improving Person Re-identifification by ...
A Multi-task Deep Network for Person Re-identification
yuanchheneducn的博客
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[A Multi-task Deep Network for Person Re-identification]
[2016,CVPR] Top-push Video-based Person Re-identification
我才是坏笨熊熊
04-17 8434
文章链接：http://isee.sysu.edu.cn/~zhwshi/Research/PreprintVersion/Top-push%20Video-based%20Person%20Re-identification.pdfAbout the authors在person re-id方面，国内做得最好的应该是中山大学的郑伟诗老师，还有一位华人，英国马里兰大学的Shaogang Gong老师
论文解读：Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation
Jingyang.Zhang的博客
09-24 1266
Motivation Template Matching-based Methods (e.g. One-shot learning for video object segmentation 没有使用时序信息)：考虑模板，比如视频中的第一帧中的mask，然后对后续帧进行匹配。但是这些方法受限于CNN分割模型的性能，不能处理具有大尺度形变和视角变化的物体。 Temporal Propagat...
CVPR2016:Improving Person Re-identification via Pose-aware Multi-shot Matching
yuanchheneducn的博客
12-02 1412
作者：Yeong-Jun Cho and Kuk-Jin Yoon Computer Vision Laboratory, GIST, South Korea动机人的外表会随着动作和视角变化而改变，因此不同姿态下进行的匹配会有很大的困难。方法1.通过估计人的姿态 （front, right, back, left） 2.建立多姿态匹配模型 3.以加权和的方式进行预测（采用KISSME学习权重
Bag of Tricks and A Strong Baseline for Deep Person Re-identification
03-31
Deep person re-identification is the task of recognizing a person across different camera views in a surveillance system. It is a challenging problem due to variations in lighting, pose, and occlusion. To address this problem, researchers have proposed various deep learning models that can learn discriminative features for person re-identification. However, achieving state-of-the-art performance often requires carefully designed training strategies and model architectures. One approach to improving the performance of deep person re-identification is to use a "bag of tricks" consisting of various techniques that have been shown to be effective in other computer vision tasks. These techniques include data augmentation, label smoothing, mixup, warm-up learning rates, and more. By combining these techniques, researchers have been able to achieve significant improvements in re-identification accuracy. In addition to using a bag of tricks, it is also important to establish a strong baseline for deep person re-identification. A strong baseline provides a foundation for future research and enables fair comparisons between different methods. A typical baseline for re-identification consists of a deep convolutional neural network (CNN) trained on a large-scale dataset such as Market-1501 or DukeMTMC-reID. The baseline should also include appropriate data preprocessing, such as resizing and normalization, and evaluation metrics, such as mean average precision (mAP) and cumulative matching characteristic (CMC) curves. Overall, combining a bag of tricks with a strong baseline can lead to significant improvements in deep person re-identification performance. This can have important practical applications in surveillance systems, where accurate person recognition is essential for ensuring public safety.
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