<div align="center"> <img width="500" height="350" src="logo.svg" alt="Awesome"> <br> <p> <a href="https://github.com/willard-yuan/awesome-cbir-papers">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.

• Classical Local Feature
• Deep Learning Feature (Global Feature)
• Deep Learning Feature (Local Feature)
• Deep Learning Feature (Instance Search)
• ANN search
• CBIR Attack
• CBIR rank
• CBIR in Industry
• CBIR Competition and Challenge
• CBIR for Duplicate(copy) detection
• Feature Fusion
• Instance Matching
• Semantic Matching
• Template Matching
• Image Identification
• Tutorials
• Slide
• Demo and Demo Online
• Datasets
• Useful Package

Classical Local Feature

• Object retrieval with large vocabularies and fast spatial matching, CVPR 2007.
• Visual Categorization with Bags of Keypoints, ECCV 2004.
• ORB: an efficient alternative to SIFT or SURF, ICCV 2011.
• Object Recognition from Local Scale-Invariant Features, ICCV 1999.
• Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval, ICCV 2007.
• Three things everyone should know to improve object retrieval, CVPR 2012.
• On-the-fly learning for visual search of large-scale image and video datasets
• All about VLAD, CVPR 2013.
• Aggregating localdescriptors into a compact image representation, CVPR 2010.
• More About VLAD: A Leap from Euclidean to Riemannian Manifolds, CVPR 2015.
• Hamming embedding and weak geometric consistency for large scale image search, CVPR 2008.
• Revisiting the VLAD image representation, project
• Improving the Fisher Kernel for Large-Scale Image Classification, ECCV 2010.
• Image Classification with the Fisher Vector: Theory and Practice
• Democratic Diffusion Aggregation for ImageRetrieval
• A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval, ACCV 2016.
• Triangulation embedding and democratic aggregation for image search, CVPR 2014.
• Efficient Large-scale Image Search With a Vocabulary Tree, IPOL 2015, code.

Deep Learning Feature (Global Feature)

• Online Invariance Selection for Local Feature Descriptors, ECCV 2020, code.
• Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval, ECCV 2020.
• SOLAR: Second-Order Loss and Attention for Image Retrieval, ECCV 2020.
• Unifying Deep Local and Global Features for Image Search, arxiv 2020.
• SOLAR: Second-Order Loss and Attention for Image Retrieval, arxiv 2020.
• A Benchmark on Tricks for Large-scale Image Retrieval，arxiv 2020.
• Learning with Average Precision: Training Image Retrieval with a Listwise Loss, ICCV 2019.
• MultiGrain: a unified image embedding for classes and instances, arxiv 2019.
• Deep Image Retrieval:Learning Global Representations for Image search.
• 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.
• Faster R-CNN Features for Instance Search, CVPR workshop 2016.
• Cross-dimensional Weighting for Aggregated Deep Convolutional Features, project.
• Class-Weighted Convolutional Features for Image Retrieval.
• Multi-Scale Orderless Pooling of Deep Convolutional Activation Features, VLAD coding.
• Aggregating Deep Convolutional Features for Image Retrieval, 论文笔记, 基于深度学习的视觉实例搜索研究进展.
• Particular object retrieval with integral max-pooling of CNN activations, project.
• Particular object retrieval using CNN.
• Learning to Match Aerial Images with Deep Attentive Architectures.
• Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval.
• Combining Fisher Vector and Convolutional Neural Networks for Image Retrieval, fv和cnn特征融合提升.
• Selective Deep Convolutional Features for Image Retrieval, ACM MM 2017.
• Class-Weighted Convolutional Features for Image Retrieval.
• Fine-tuning CNN Image Retrieval with No Human Annotation, TPAMI 2018.
• An accurate retrieval through R-MAC+ descriptors for landmark recognition.
• Regional Attention Based Deep Feature for Image Retrieval, code, BMVC 2018.
• Detect-to-Retrieve: Efficient Regional Aggregation for Image Search, CVPR 2019.
• Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking, project, CVPR 2018.
• Guided Similarity Separation for Image Retrieval, NeurIPS 2019.

Deep Learning Feature (Local Feature)

• Glue Factory is CVG's library for training and evaluating deep neural network that extract and match local visual feature, code
• DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching, arXiv 2023, code.
• LightGlue: Local Feature Matching at Light Speed, arXiv 2023, code.
• Simple Learned Keypoints, a self-supervised deep learning keypoint model, arxiv 2023, code.
• Learning Super-Features for Image Retrieval, ICLR 2022, code.
• LoFTR: Detector-Free Local Feature Matching with Transformers, CVPR 2021, code.
• DFM: A Performance Baseline for Deep Feature Matching, CVPRW 2021, code.
• COTR: Correspondence Transformer for Matching Across Images, arxiv 2021.
• Online Invariance Selection for Local Feature Descriptors, ECCV 2020, code.
• Learning and aggregating deep local descriptors for instance-level recognition, ECCV 2020, code.
• DISK: Learning local features with policy gradient, NeurIPS 2020, code.
• Learning and aggregating deep local descriptorsfor instance-level recognition, ECCV 2020, code.
• [D2D: Keypoint Extraction