- A Benchmark on Tricks for Large-scale Image Retrieval，通用图像检索各种trick介绍。
- Learning with Average Precision: Training Image Retrieval with a Listwise Loss, deep image retrieval续作。
- MultiGrain: a unified image embedding for classes and instances，Hervé Jégou, Andrea Vedaldi, Matthijs Douze，三牛首次出现在同一paper中。将相似和实例检索统一在一个框架中。
- Composing Text and Image for Image Retrieval - An Empirical Odyssey
- Visualizing Deep Similarity Networks
- Combination of Multiple Global Descriptors for Image Retrieval
- Object retrieval with large vocabularies and fast spatial matching
- Visual Categorization with Bags of Keypoints
- ORB: an efficient alternative to SIFT or SURF
- Object Recognition from Local Scale-Invariant Features
- Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
- Three things everyone should know to improve object retrieval
- On-the-fly learning for visual search of large-scale image and video datasets
- All about VLAD
- Aggregating localdescriptors into a compact image representatio
- More About VLAD: A Leap from Euclidean to Riemannian Manifolds
- Hamming embedding and weak geometric consistency for large scale image search
- Revisiting the VLAD image representation, project
- Improving the Fisher Kernel for Large-Scale Image Classification
- 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
- Triangulation embedding and democratic aggregation for image search
- Efficient Large-scale Image Search With a Vocabulary Tree, code
- 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
- 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
- Class-Weighted Convolutional Features for Image Retrieval
- Towards Good Practices for Image Retrieval Based on CNN Features
- Fine-tuning CNN Image Retrieval with No Human Annotation
- 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, arxiv.
- Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking, project, CVPR 2018.
- Guided Similarity Separation for Image Retrieval, NeurIPS 2019.
- Learning Discriminative Affine Regions via Discriminability, affnet
- A Large Dataset for Improving Patch Matching, PS-Dataset
- Working hard to know your neighbor's margins: Local descriptor learning loss, hardnet
- MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching, matchnet
- LF-Net: Learning Local Features from Images, NeurIPS 2018.
- Local Descriptors Optimized for Average Precision, CVPR 2018
- SuperPoint: Self-Supervised Interest Point Detection and Description, Magic Leap
- GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints, code, ECCV 2018.
- Learning local feature descriptors with triplets and shallow convolutional neural networks, BMVC 2016.
- SOSNet: Second Order Similarity Regularization for Local Descriptor Learning, CVPR 2019.
- Local Features and Visual Words Emerge in Activations, CVPR 2019.
- Explicit Spatial Encoding for Deep Local Descriptors, CVPR 2019.
- Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters, ICCV 2019.
- Learning to Route in Similarity Graphs, ICML 2019.
- Practical and Optimal LSH for Angular Distance
- pq-fast-scan
- faiss. A library for efficient similarity search and clustering of dense vectors.
- Polysemous codes
- Optimized Product Quantization
- lopq. Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark.
- nns_benchmark. Benchmark of Nearest Neighbor Search on High Dimensional Data.
- Optimized Product Quantization
- Falconn. FAst Lookups of Cosine and Other Nearest Neighbors.
- Annoy. Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
- NMSLIB. Non-Metric Space Library (NMSLIB): A similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.
- Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs, graph-based method.
- Fast Approximate Nearest Neighbor Search With Navigating Spreading-out Graphs, code
- Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition
- NV-tree: A Scalable Disk-Based High-Dimensional Index
- Dynamicity and Durability in Scalable Visual Instance Search
- Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors，code
- Link and code: Fast indexing with graphs and compact regression codes
- A Survey of Product Quantization，对于矢量量化方法一篇比较完整的调研，值得一读
- GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints，学习局部特征的descriptor，匹配能力较强
- Learning a Complete Image Indexing Pipeline, CVPR 2018
- spreading vectors for similarity search, ICLR 2019.
- SPTAG: A library for fast approximate nearest neighbor search. Microsoft.
- Fast Spectral Ranking for Similarity Search, code, CVPR 2018
- Videntifier is a visual search engine based on a patented large-scale local feature database, demo, based on SIFT feature and NV-tree.
- Web-Scale Responsive Visual Search at Bing
- Visual Search at Pinterest
- Visual Discovery at Pinterest
- Visual Search at ebay
- Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce, project
- Google Landmark Retrieval Challenge, 2018
- Alibaba Large-scale Image Search Challenge, 2015
- Pkbigdata image retrieval, 2015
- Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset, Landmark2019-1st-and-3rd-Place-Solution.
- Graph-Cut RANSAC, code
- Image Matching Benchmark
- GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence
- A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval
- CODE: Coherence Based Decision Boundaries for Feature Correspondence
- Robust feature matching in 2.3µs
- PopSift is an implementation of the SIFT algorithm in CUDA
- openMVG robust_estimation
- Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses.
- Homography from two orientation- and scale-covariant features, code.
- Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations
- How to Apply Distance Metric Learning to Street-to-Shop Problem
- Recent Image Search Techniques
- Compact Features for Visual Search
- multimedia-indexing. A framework for large-scale feature extraction, indexing and retrieval.
- Image Similarity using Deep Ranking, code.
- Triplet Loss and Online Triplet Mining in TensorFlow
- tf_retrieval_baseline.
- VRG Prague in “Large-Scale Landmark Recognition Challenge”, ranked 3rd in the Google Landmark Recognition Challenge.
- Visual Image Retrieval and Localization, SIFT feature encoded by BOW.
- VGG Image Search Engine, SIFT feature encoded by BOW.
- SoTu, A flask-based cbir system.
- yisou, A flask-based painting cbir system, the search algorithm is designed by Yong Yuan.
- DeepFashion2 Dataset, DeepFashion2 is a comprehensive fashion dataset.