awesome-cbir-papers::memo:超棒和经典的图像检索论文

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很棒的图像检索论文 主要目标是收集学术界和工业界中经典而可靠的图像检索作品。 语义匹配 模板匹配 图像识别 讲解 滑动 演示和在线演示 数据集 有用的包裹 古典地方特色 具有大词汇量和快速空间匹配的对象检索,CVPR 2007。 带关键点的视觉分类,ECCV 2004。 ORB:一种有效替代SIFT或SURF的方法,ICCV 2011。 来自局部尺度不变特征的对象识别,ICCV 1999。 全面召回:具有查询对象生成功能模型的自动查询扩展,ICCV 2007。 每个人都应该了解的三件事,以改进对象检索,CVPR 2012。 动态学习,用于可视化大规模图像和视频数据集 关于VLAD ,CVPR 2013。 将localdescriptor聚合为紧凑的图像表示形式,CVPR 2010。 有关VLAD的更多信息:从欧几里得到黎曼流形的飞跃,CVPR 2015。 用于大规模图像
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内容介绍
<div align="center"> <br> <p> <a href="https://github.com/willard-yuan/awesome-cbir-papers" rel='nofollow' onclick='return false;'>CBIR in academia and industry</a> </p> </div> # Awesome image retrieval papers The main goal is to collect classical and solid works of image retrieval in academia and industry. [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) - [Classical Local Feature](#classical-local-feature) - [Deep Learning Feature (Global Feature)](#deep-learning-feature-global-feature) - [Deep Learning Feature (Local Feature)](#deep-learning-feature-local-feature) - [Deep Learning Feature (Instance Search)](#deep-learning-feature-instance-search) - [ANN search](#ann-search) - [CBIR Attack](#cbir-attack) - [CBIR rank](#cbir-rank) - [CBIR in Industry](#cbir-in-industry) - [CBIR Competition and Challenge](#cbir-competition-and-challenge) - [CBIR for Duplicate(copy) detection](#cbir-for-duplicatecopy-detection) - [Feature Fusion](#feature-fusion) - [Instance Matching](#instance-matching) - [Semantic Matching](#semantic-matching) - [Template Matching](#template-matching) - [Image Identification](#image-identification) - [Tutorials](#tutorials) - [Slide](#slide) - [Demo and Demo Online](#demo-and-demo-online) - [Datasets](#datasets) - [Useful Package](#useful-package) ## Classical Local Feature - [Object retrieval with large vocabularies and fast spatial matching](https://www.robots.ox.ac.uk/~vgg/publications/papers/philbin07.pdf), CVPR 2007. - [Visual Categorization with Bags of Keypoints](http://www.cs.princeton.edu/courses/archive/fall09/cos429/papers/csurka-eccv-04.pdf), ECCV 2004. - [ORB: an efficient alternative to SIFT or SURF](https://www.willowgarage.com/sites/default/files/orb_final.pdf), ICCV 2011. - [Object Recognition from Local Scale-Invariant Features](http://www.cs.ubc.ca/~lowe/papers/iccv99.pdf), ICCV 1999. - [Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval](https://www.robots.ox.ac.uk/~vgg/publications/papers/philbin07.pdf), ICCV 2007. - [Three things everyone should know to improve object retrieval](https://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf), CVPR 2012. - [On-the-fly learning for visual search of large-scale image and video datasets](https://www.robots.ox.ac.uk/~vgg/publications/2015/Chatfield15/chatfield15.pdf) - [All about VLAD](), CVPR 2013. - [Aggregating localdescriptors into a compact image representation](https://lear.inrialpes.fr/pubs/2010/JDSP10/jegou_compactimagerepresentation.pdf), CVPR 2010. - [More About VLAD: A Leap from Euclidean to Riemannian Manifolds](https://paperswithcode.com/paper/more-about-vlad-a-leap-from-euclidean-to), CVPR 2015. - [Hamming embedding and weak geometric consistency for large scale image search](https://lear.inrialpes.fr/pubs/2008/JDS08/jegou_hewgc08.pdf), CVPR 2008. - [Revisiting the VLAD image representation](https://hal.inria.fr/hal-00840653v1/document), [project](https://github.com/jorjasso/VLAD/blob/master/VLADlib/VLAD.py) - [Improving the Fisher Kernel for Large-Scale Image Classification](https://www.robots.ox.ac.uk/~vgg/rg/papers/peronnin_etal_ECCV10.pdf), ECCV 2010. - [Image Classification with the Fisher Vector: Theory and Practice](https://hal.inria.fr/hal-00830491/document) - [Democratic Diffusion Aggregation for ImageRetrieval]() - [A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval](https://www.microsoft.com/en-us/research/uploads/prod/2019/09/accv_2016_schoenberger.pdf), ACCV 2016. - [Triangulation embedding and democratic aggregation for image search](https://www.robots.ox.ac.uk/~vgg/publications/2014/Jegou14/jegou14.pdf), CVPR 2014. - [Efficient Large-scale Image Search With a Vocabulary Tree](http://www.ipol.im/pub/art/2018/199/), IPOL 2015, [code](https://github.com/fragofer/voctree). ## Deep Learning Feature (Global Feature) - [Online Invariance Selection for Local Feature Descriptors](https://arxiv.org/abs/2007.08988), ECCV 2020, [code](https://github.com/rpautrat/LISRD). - [Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval](https://arxiv.org/pdf/2007.12163.pdf), ECCV 2020. - [SOLAR: Second-Order Loss and Attention for Image Retrieval](https://arxiv.org/pdf/2001.08972.pdf), ECCV 2020. - [Unifying Deep Local and Global Features for Image Search](https://arxiv.org/abs/2001.05027), arxiv 2020. - [SOLAR: Second-Order Loss and Attention for Image Retrieval](https://arxiv.org/abs/2001.08972v2), arxiv 2020. - [A Benchmark on Tricks for Large-scale Image Retrieval](https://arxiv.org/pdf/1907.11854.pdf),arxiv 2020. - [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://arxiv.org/pdf/1906.07589v1.pdf), ICCV 2019. - [MultiGrain: a unified image embedding for classes and instances](https://arxiv.org/abs/1902.05509), arxiv 2019. - [Deep Image Retrieval:Learning Global Representations for Image search](https://arxiv.org/abs/1604.01325). - [End-to-end Learning of Deep Visual Representations for Image retrieval](https://arxiv.org/abs/1610.07940), DIR更详细的论文说明. - [What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?](https://arxiv.org/abs/1611.01640), 关于layer选取的问题. - [Bags of Local Convolutional Features for Scalable Instance Search](https://arxiv.org/abs/1604.01325). - [Faster R-CNN Features for Instance Search](https://github.com/imatge-upc/retrieval-2016-deepvision), CVPR workshop 2016. - [Cross-dimensional Weighting for Aggregated Deep Convolutional Features](https://arxiv.org/abs/1512.04065), [project](https://github.com/yahoo/crow). - [Class-Weighted Convolutional Features for Image Retrieval](https://github.com/imatge-upc/retrieval-2017-cam). - [Multi-Scale Orderless Pooling of Deep Convolutional Activation Features](), VLAD coding. - [Aggregating Deep Convolutional Features for Image Retrieval](https://arxiv.org/abs/1510.07493), [论文笔记](https://zhuanlan.zhihu.com/p/23136747), [基于深度学习的视觉实例搜索研究进展](https://zhuanlan.zhihu.com/p/22265265). - [Particular object retrieval with integral max-pooling of CNN activations](https://arxiv.org/abs/1511.05879), [project](http://cmp.felk.cvut.cz/~toliageo/soft.html). - [Particular object retrieval using CNN](https://github.com/AaltoVision/Object-Retrieval). - [Learning to Match Aerial Images with Deep Attentive Architectures](https://vision.cornell.edu/se3/wp-content/uploads/2016/04/1204.pdf). - [Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval](https://arxiv.org/pdf/1702.00338v1.pdf). - [Combining Fisher Vector and Convolutional Neural Networks for Image Retrieval](http://ceur-ws.org/Vol-1653/paper_19.pdf), fv和cnn特征融合提升. - [Selective Deep Convolutional Features for Image Retrieval](https://arxiv.org/pdf/1707.00809v1.pdf), ACM MM 2017. - [Class-Weighted Convolutional Features for Image Retrieval](https://github.com/imatge-upc/retrieval-2017-cam). - [Fine-tuning CNN Image Retrieval with No Human Annotation](https://arxiv.org/abs/1711.02512), TPAMI 2018. - [An accurate retrieval through R-MAC+ descriptors for landmark recognition](https://arxiv.org/pdf/1806.08565.pdf). - [Regional Attention Based Deep Feature for Image Retrieval](https://sglab.kaist.ac.kr/RegionalAttention/), [code](https://github.com/jaeyoon1603/Retrieval-RegionalAttention), BMVC 2018. - [Detect-to-Retrieve: Efficient Regional Aggregation for Image Search](https://arxiv.org/pdf/1812.01584.pdf), CVPR 2019. - [Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking](http://cmp.felk.cvut.cz/~toliageo/p/RadenovicIscenToliasAvrithisChum_CVPR2018_Revisiting%20Oxford%20and%20Paris:%20Large-Scale%20Image%20Retrieval%20Benchmarking.pdf), [project](http://cmp.felk.cvut.cz/revisitop/), CVPR 2018. - [Guided Similarity Separation for Image Retrieval](https://github.com/layer6ai-labs/GSS), NeurIPS 2019. ## Deep Learning Feature (Local Feature)
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