Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/lgbwust/awesome-image-retrieval-papers

List: awesome-image-retrieval-papers
Last synced: about 2 months ago
JSON representation
Host: GitHub
URL: https://github.com/lgbwust/awesome-image-retrieval-papers
Owner: lgbwust
Created: 2015-10-26T06:05:22.000Z (about 9 years ago)
Default Branch: master
Last Pushed: 2018-12-06T04:29:56.000Z (about 6 years ago)
Last Synced: 2024-10-28T02:51:37.192Z (about 2 months ago)
Size: 10.7 KB
Stars: 36
Watchers: 4
Forks: 6
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

awesomeai - Awesome image retrieval papers (2)
awesome-ai-awesomeness - Awesome image retrieval papers (2)
awesome-ai-awesomeness - Awesome image retrieval papers (2)
README

        # some awesome image retrieval papers

> 1、Deep Learning for Content-Based Image Retrieval

> 2、Online Multimodal Deep Similarity Learning with Application to Image Retrieval

> 3、Neural Codes for Image Retrieval

> 4、A practical guide to CNNs and Fisher Vectors for image instance retrieval

> 5、Convolutional Neural Codes for Image Retrieval

> 6、Deep Convolutional Features for Image Based Retrieval and Scene Categorization

> 7、Deep Hashing for Compact Binary Codes Learning

> 8、Deep Learning of Binary Hash Codes for Fast Image Retrieval

> 9、Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval

> 10、Efficient Manifold Ranking for Image Retrieval

> 11、Iterative Quantization A Procrustean Approach to Learning Binary Codes

> 12、Large Scale Online Learning of Image Similarity Through Ranking

> 13、Learning Hash Functions Using Sparse Reconstruction

> 14、Using Very Deep Autoencoders for Content-Based Image Retrieval

> 15、Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search

-----------------update-------------------

## Awesome image retrieval papers

#### Local Feature Based

- [Object retrieval with large vocabularies and fast spatial matching](https://www.robots.ox.ac.uk/~vgg/publications/papers/philbin07.pdf)

- [Improving the Fisher Kernel for Large-Scale Image Classification](https://www.robots.ox.ac.uk/~vgg/rg/papers/peronnin_etal_ECCV10.pdf)

- [Visual Categorization with Bags of Keypoints](http://www.cs.princeton.edu/courses/archive/fall09/cos429/papers/csurka-eccv-04.pdf)

- [ORB: an efficient alternative to SIFT or SURF](https://www.willowgarage.com/sites/default/files/orb_final.pdf)

- [Object Recognition from Local Scale-Invariant Features](http://www.cs.ubc.ca/~lowe/papers/iccv99.pdf)

- [Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval](https://www.robots.ox.ac.uk/~vgg/publications/papers/philbin07.pdf)

- [Three things everyone should know to improve object retrieval](https://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf)

- [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)

#### Deep Learning Feature Based

- [Deep Image Retrieval:Learning Global Representations for Image earch](https://arxiv.org/abs/1604.01325)

- [End-to-end Learning of Deep Visual Representations for Image retrieval](), DIR更详细的论文说明

- [What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?](), 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)

- [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)

- [Class-Weighted Convolutional Features for Image Retrieval](https://github.com/imatge-upc/retrieval-2017-cam)

#### ANN search

- [Practical and Optimal LSH for Angular Distance](chrome-extension://ikhdkkncnoglghljlkmcimlnlhkeamad/pdf-viewer/web/viewer.html?file=http%3A%2F%2Fpapers.nips.cc%2Fpaper%2F5893-practical-and-optimal-lsh-for-angular-distance.pdf)

- [pq-fast-scan](https://github.com/technicolor-research/pq-fast-scan)

- [faiss](https://github.com/facebookresearch/faiss). A library for efficient similarity search and clustering of dense vectors.

- [lopq](https://github.com/yahoo/lopq). Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark.

- [nns_benchmark](https://github.com/DBWangGroupUNSW/nns_benchmark). Benchmark of Nearest Neighbor Search on High Dimensional Data.

- [Optimized Product Quantization](http://kaiminghe.com/cvpr13/index.html)

- [Falconn](https://github.com/FALCONN-LIB/FALCONN). FAst Lookups of Cosine and Other Nearest Neighbors.

- [Annoy](https://github.com/spotify/annoy). Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk 

- [NMSLIB](https://github.com/searchivarius/nmslib). Non-Metric Space Library (NMSLIB): A similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.

#### CBIR in Industry

- [Videntifier](http://flickrdemo.videntifier.com/) is a visual search engine based on a patented large-scale local feature database

- [Web-Scale Responsive Visual Search at Bing](https://arxiv.org/abs/1802.04914)

- [Visual Search at Pinterest](https://labs.pinterest.com/user/themes/pinlabs/assets/paper/visual_search_at_pinterest.pdf)

- [Visual Discovery at Pinterest](https://arxiv.org/abs/1702.04680)

- [Visual Search at ebay]()

- [Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce](https://arxiv.org/abs/1703.02344), [project](https://github.com/flipkart-incubator/fk-visual-search)

#### feature fusion

- [Feature fusion using Canonical Correlation Analysis](https://github.com/mhaghighat/ccaFuse)

#### Feature Matching

- [Image Matching Benchmark](https://arxiv.org/pdf/1709.03917.pdf)

- [GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence]()

- [A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval](https://github.com/vote-and-verify/vote-and-verify)

- [CODE: Coherence Based Decision Boundaries for Feature Correspondence]()

- [Robust feature matching in 2.3µs](https://www.edwardrosten.com/work/taylor_2009_robust.pdf)

- [PopSift is an implementation of the SIFT algorithm in CUDA](https://github.com/alicevision/popsift)

- [openMVG robust_estimation](https://github.com/openMVG/openMVG/tree/e3a0bde5e9c676d1cb663a38f7e74c771324d69a/src/openMVG/robust_estimation)

#### Plan to read

- [VisualRank: Applying PageRank to Large-Scale Image Search]()

### Tutorials

- [Recent Image Search Techniques](http://cvpr2016.thecvf.com/program/tutorials)

- [Compact Features for Visual Search](http://cvpr2016.thecvf.com/program/tutorials)

- [multimedia-indexing](https://github.com/MKLab-ITI/multimedia-indexing). A framework for large-scale feature extraction, indexing and retrieval.

## Awesome multiclass classification

### papers

- [Loss Functions for Top-k Error: Analysis and Insights]() and [Top-k Multiclass SVM](), [code](https://github.com/mlapin/libsdca)

### Tutorials

- [Linear Classification](http://cs231n.github.io/linear-classify/), [中文版](http://blog.csdn.net/elaine_bao/article/details/50519970), [demo](http://vision.stanford.edu/teaching/cs231n/linear-classify-demo/)

---

## Logo Detection and Classification

### Papers

- [LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks](https://arxiv.org/abs/1511.02462)

---

## Object Detection and Recognition

### Papers

- [SSD: Single Shot MultiBox Detector](www.cs.unc.edu/~wliu/papers/ssd.pdf), [code](https://github.com/weiliu89/caffe/tree/ssd)

- [A Closer Look: Small Object Detection in Faster R-CNN]()

---

## Video Classification

### Papers

- [Large-scale Video Classification with Convolutional Neural Networks](vision.stanford.edu/pdf/karpathy14.pdf)

- [Learning Spatiotemporal Features With 3D Convolutional Networks](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Tran_Learning_Spatiotemporal_Features_ICCV_2015_paper.pdf), [code](https://github.com/Lasagne/Recipes/blob/master/examples/Video%20features%20with%20C3D.ipynb), [doc](https://docs.google.com/document/d/1-QqZ3JHd76JfimY4QKqOojcEaf5g3JS0lNh-FHTxLag/edit), [project](http://vlg.cs.dartmouth.edu/c3d/)

- [ActionVLAD: Learning spatio-temporal aggregation for action classification](https://rohitgirdhar.github.io/ActionVLAD/)

---

## ROS for Robotics

- [Learning_ROS_for_Robotics_Programming_2nd_edition](https://github.com/AaronMR/Learning_ROS_for_Robotics_Programming_2nd_edition)

---

## Image Fingerprinting

- [Video fingerprinting tool. Finding duplicate movies in a large dataset](https://github.com/funzoneq/video_fingerprinting)

-----------------update-------------------

### CVPR 2018

- [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

- [Fast Spectral Ranking for Similarity Search](http://cn.arxiv.org/pdf/1703.06935.pdf), [code](https://github.com/ducha-aiki/manifold-diffusion), CVPR 2018

- [Learning a Complete Image Indexing Pipeline](https://arxiv.org/pdf/1712.04480.pdf), CVPR 2018

#### Deep Learning Feature (Global Feature)

- [Towards Good Practices for Image Retrieval Based on CNN Features]()

- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://arxiv.org/abs/1711.02512)

- [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.

#### Deep Learning Feature (Local Feature)

- [Learning Discriminative Affine Regions via Discriminability](http://cn.arxiv.org/pdf/1711.06704.pdf), [affnet](https://github.com/ducha-aiki/affnet)

- [A Large Dataset for Improving Patch Matching](http://cn.arxiv.org/pdf/1801.01466.pdf), [PS-Dataset](https://github.com/rmitra/PS-Dataset)

- [Working hard to know your neighbor's margins: Local descriptor learning loss](), [hardnet](https://github.com/DagnyT/hardnet)

- [MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching](), [matchnet](https://github.com/hanxf/matchnet)

- [LF-Net: Learning Local Features from Images](https://arxiv.org/abs/1805.09662), NeurIPS 2018.

- [Local Descriptors Optimized for Average Precision](http://openaccess.thecvf.com/content_cvpr_2018/papers/He_Local_Descriptors_Optimized_CVPR_2018_paper.pdf), CVPR 2018

- [SuperPoint: Self-Supervised Interest Point Detection and Description](http://cn.arxiv.org/pdf/1712.07629.pdf), Magic Leap

- [GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints](https://arxiv.org/pdf/1807.06294.pdf), [code](https://github.com/lzx551402/geodesc), ECCV 2018.

- [Learning local feature descriptors with triplets and shallow convolutional neural networks](https://github.com/vbalnt/tfeat), BMVC 2016.

#### CBIR Competition and Challenge

- [Google Landmark Retrieval Challenge](https://www.kaggle.com/c/landmark-retrieval-challenge), 2018

- [Alibaba Large-scale Image Search Challenge](https://tianchi.aliyun.com/competition/introduction.htm?raceId=231510&_lang=en_US), 2015

- [Pkbigdata image retrieval](http://www.pkbigdata.com/common/cmpt/%E5%9B%BE%E5%83%8F%E6%90%9C%E7%B4%A2%E7%AB%9E%E8%B5%9B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html]), 2015

#### CBIR for Duplicate(copy) detection

- [A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwiisbW0maXYAhXLOY8KHUw0AEsQFgg7MAI&url=https%3A%2F%2Fpdfs.semanticscholar.org%2F7b4f%2F68e227999da8ffc6dc9f7fd34da5ebaad09f.pdf&usg=AOvVaw0mZvcT7VhEuEm68oieXLv-)

#### Instance Matching

- [Graph-Cut RANSAC](https://arxiv.org/abs/1706.00984), [code](https://github.com/danini/graph-cut-ransac)

- [Image Matching Benchmark](https://arxiv.org/pdf/1709.03917.pdf)

- [GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence](https://github.com/JiawangBian/GMS-Feature-Matcher)

- [A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval](https://github.com/vote-and-verify/vote-and-verify)

- [CODE: Coherence Based Decision Boundaries for Feature Correspondence]()

- [Robust feature matching in 2.3µs](https://www.edwardrosten.com/work/taylor_2009_robust.pdf)

- [PopSift is an implementation of the SIFT algorithm in CUDA](https://github.com/alicevision/popsift)

- [openMVG robust_estimation](https://github.com/openMVG/openMVG/tree/e3a0bde5e9c676d1cb663a38f7e74c771324d69a/src/openMVG/robust_estimation)