Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
📝Awesome and classical image retrieval papers
| Date | Stars |
|---|---|
| 2026-07-30 | 1760 |
| 2026-08-17 | 1758 |
| 2026-08-29 | 1757 |
| 2026-09-15 | 1759 |
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| 2026-09-29 | 1761 |
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| 2026-10-20 | 1761 |
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| 2026-11-04 | 1761 |
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<img width="500" height="350" src="logo.svg" alt="Awesome">
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<a href="https://github.com/willard-yuan/awesome-cbir-papers">CBIR in academia and industry</a>
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# Awesome image retrieval papers
The main goal is to collect classical and solid works of image retrieval in academia and industry.
[](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](https://www.robots.ox.ac.uk/~vgg/publications/2013/arandjelovic13/arandjelovic13.pdf), 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 FeatureExcerpt of 25,870 characters
Read on GitHub
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Zhedong Zheng · University of Macau · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:61f1bf13b44a6d02, topic:nearest-neighbor-search