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<!DOCTYPE html>
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<a href="/2021/10/30/Multimodal_search_20211030/" class="post-title-link" itemprop="url">Joint Visual-Textual Embedding for Multimodal Style Search (图像+文本多模态检索)</a>
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文章总结视觉时装搜索时能够操纵属性是一个有意义的场景。文章的背景主要是应用导向的。面向的场景是，用户在搜索一个商品时，给出一张照片和一些文本描述，并通过文本描述修改照片的某些内容。本文的解决思路是将图像和文本嵌入到一个公共空间，通过查询给出的图像嵌入和文本嵌入之间的算术运算构建该空间中的一个新向量（
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<a href="/2021/10/30/Link_and_code_2021/" class="post-title-link" itemprop="url">CVPR2018-Link and code：Fast indexing with graphs and compact regression codes (图结构提升向量编码精度)</a>
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论文概述相似性搜索是计算机视觉的一个基础模块。早期，一个图像将会对应几千个向量，因此，使用compact representation非常重要。最近，一个图像对应一个向量（通常从卷积神经网络的激活层提取）。
本文大致思路：（1）通过OPQ 压缩原始向量；（2）根据邻居执行量化回归不断优化量化码。通过
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<a href="/2021/09/18/MRNG_theory/" class="post-title-link" itemprop="url">基于近邻图的向量检索算法：单调相对邻域图MRNG的一些重要理论性质</a>
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论文信息题目: Understanding and Generalizing Monotonic Proximity Graphs for Approximate Nearest Neighbor Search
作者: Dantong Zhu, Minjia Zhang; 于2021年7月27发表在
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概览本文是参考文献[1]的简介，这篇论文是一篇tutorial（ICDE2021），主要介绍一些高维相似性搜索的数据科学应用，调查了最近的一些方法，讨论了AI驱动的、渐进式的以及分布式的相似性搜索。
应用自动实体解析、数据发现、电力需求分析、推荐系统、聚类、划分、异常检测、生物信息、计算机视觉、安防
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需求想要在linux服务器上快速执行一些操作，或者至少能有效执行一些操作，毕竟最近git clone这种都比较奢侈了。 参考其他/她大佬的博客时，没有见到比较完整的教程，因此把整个过程写在下面。
准备
xrdp（远程ubuntu桌面才需要）
Qv2ray
v2ray(配合Qv2ray)
proxyc
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导言为什么需要向量相似性检索？前人已有很多优秀的回答：参考1，参考2，参考3。总之，向量检索是很多AI应用的一个基础模块，它是与AI领域的发展共荣共损的。近年来，学术界和工业界提出了很多优秀的算法以及后续优化来应对高维向量的检索问题，这些方法旨在为向量检索提供一个更好的解决方案。然而，无论从算法设计
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论文题目Multiattribute Approximate Nearest Neighbor Search Based on Navigable Small World Graph
相关信息作者与单位Xiaoliang Xu | Chang Li | Yuxiang Wang | Yixing X
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