awesome-cbir-papers
github.com/willard-yuan/awesome-cbir-papers ↗📝Awesome and classical image retrieval papers
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Classical Local FeatureDeep Learning Feature (Global Feature)Deep Learning Feature (Local Feature)Deep Learning Feature (Instance Search)ANN searchCBIR AttackCBIR rankCBIR in IndustryCBIR Competition and ChallengeCBIR for Duplicate(copy) detectionFeature FusionInstance MatchingSemantic MatchingTemplate MatchingImage IdentificationTutorialsSlideDemo and Demo OnlineDatasetsUseful Package
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me deep learning feature (global feature) resources from awesome-cbir-papers"
Installation instructions →What's inside
Deep Learning Feature (Global Feature)
- A Benchmark on Tricks for Large-scale Image Retrieval
- Aggregating Deep Convolutional Features for Image Retrieval
- An accurate retrieval through R-MAC+ descriptors for landmark recognition
- Bags of Local Convolutional Features for Scalable Instance Search
- Class-Weighted Convolutional Features for Image Retrieval
- Combining Fisher Vector and Convolutional Neural Networks for Image Retrieval
ANN search
- Accelerating Large-Scale Inference with Anisotropic Vector Quantization
- Annoy
- A Survey of Product Quantization
- Dynamicity and Durability in Scalable Visual Instance Search
- Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
- Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition
Instance Matching
- AdaLAM: Revisiting Handcrafted Outlier Detection
- A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval
- GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence
- Graph-Cut RANSAC
- Homography from two orientation- and scale-covariant features
- Image Matching Benchmark
Classical Local Feature
- Aggregating localdescriptors into a compact image representation
- All about VLAD
- A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval
- Efficient Large-scale Image Search With a Vocabulary Tree
- Hamming embedding and weak geometric consistency for large scale image search
- Image Classification with the Fisher Vector: Theory and Practice
Deep Learning Feature (Local Feature)
CBIR Competition and Challenge
CBIR for Duplicate(copy) detection
Showing a sample of 161 resources. View the full list on GitHub →