dengchengcheng / awesome-local-global-descriptor

My personal note about local and global descriptor

awesome-local-global-descriptor

This is my personal note about local and global descriptor. Trying to make anyone can get in to these fields more easily. If you find anything you want to add, feel free to post on issue or email me.

This repo will be constantly updated.

Author: Tsun-Yi Yang ([e-mail])

Local matching pipeline

In this section, I focus on the review about the sparse keypoint matching and it's pipeline.

1. Keypoint detection

This subsection includes the review about keypoint detection and it's orientation, scale, or affine transformation estimation.

Year	Paper	link	Code
[ICCV19]	Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters	PDF	Github
[ECCV18]	Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability	arXiv	Github
[CVPR17]	Quad-networks: unsupervised learning to rank for interest point detection	PDF	-
[CVPR16]	Learning to Assign Orientations to Feature Poitns	-	Github
[CVPR15]	TILDE: a Temporally Invariant Learned DEtector	arXiv	Github

2. Keypoint description (local descriptor)

In the last few decades, people focus on the patch descriptor

• Hand-crafted

Year	Paper	link	Code
[CVPR16]	Accumulated Stability Voting: A Robust Descriptor from Descriptors of Multiple Scales	PDF	Github
[CVPR15]	Domain-Size Pooling in Local Descriptors: DSP-SIFT	PDF	-
[CVPR15]	BOLD - Binary Online Learned Descriptor For Efficient Image Matching	PDF	Github
[CVPR13]	Boosting binary keypoint descriptors	-	-
[CVPR12]	Freak: Fast retina keypoint	-	-
[CVPR12]	Three things everyone should know to improve object retrieval	PDF	-
[IPOL11]	ASIFT: An Algorithm for Fully Affine Invariant Comparison	-	-
[ICCV11]	BRISK: Binary robust invariant scalable keypoints	-	-
[ICCV11]	Orb: An efficient alternative to sift or surf	-	-
[ICCV11]	Local inten-sity order pattern for feature description	-	-
[CVIU06]	Speeded-up robust features (SURF)	-	-
[ECCV06]	Surf:Speeded up robust features	-	-
[IJCV04]	Distinctive image features from scale-invariant keypoints	-	Github

• Deep learning

Year	Paper	link	Code
[ICCV19]	Beyond Cartesian Representations for Local Descriptors	PDF	-
[CVPR19]	SOSNet: Second Order Similarity Regularization for Local Descriptor Learning	arXiv,Page	Github
[ECCV18]	GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints	-	Github
[CVPR18]	Local Descriptors Optimized for Average Precision	Page	-
[NIPS17]	Working hard to know your neighbor's margins: Local descriptor learning loss	arXiv	Github
[ICCV17]	DeepCD: Learning Deep Complementary Descriptors for Patch Representations	PDF	Github
[CVPR17]	L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space	PDF	Github
[arXiv16]	PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors	arXiv	Github
[BMVC16]	Learning local feature descriptors with triplets and shallow convolutional neural networks	PDF	Github
[ICCV15]	Discriminative Learning of Deep Convolutional Feature Point Descriptors	Page	Github
[CVPR15]	MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching	PDF	-
[CVPR15]	Learning to compare image patches via convolutional neural networks	PDF	Github

3. End-to-end matching pipeline

Recently, more and more papers try to embed the whole matching pipeline (keypoint detection, keypoint description) into one framework.

Year	Paper	link	Code
[arXiv19]	Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task	arXiv	-
[NIPS19]	R2D2: Repeatable and Reliable Detector and Descriptor	arXiv,Page	-
[ICCV19]	ELF: Embedded Localisation of Features in Pre-Trained CNN	PDF	Github
[CVPR19]	D2-Net: A Trainable CNN for Joint Description and Detection of Local Features	arXiv,Page	Github
[CVPRW18]	SuperPoint: Self-Supervised Interest Point Detection and Description	arXiv	Github
[NIPS18]	LF-Net: Learning Local Features from Images	PDF	Github
[ECCV16]	LIFT: Learned Invariant Feature Points	-	Github

4. Geometric verification or learning based matcher

After the matching, standard RANSAC and it's variants are usually adopted for outlier removal.

• Algorithm based

Year	Paper	link	Code
[CVPR19]	MAGSAC: Marginalizing Sample Consensus	PDF	Github
[ECCV12]	Improving Image-Based Localization by Active Correspondence Search	PDF	-
[CVPR05]	Matching with PROSAC – Progressive Sample Consensus	PDF	-

• Deep learning based

Year	Paper	link	Code
[arXiv19]	SuperGlue: Learning Feature Matching with Graph Neural Networks	arXiv	-
[ICCV19]	NG-RANSAC for Epipolar Geometry from Sparse Correspondences	arXiv	Github
[ICCV19]	Learning Two-View Correspondences and Geometry Using Order-Aware Network	arXiv	Github
[CVPR18]	Learning to Find Good Correspondences	-	Github

Global retrieval

Consider global retrieval usually targets on a lot of candidates, there are several way to generate one single description for one image.

1. Feature aggregation

• Hand-crafted

When there is only hand-crafted local descriptors, people usually uses feature aggregation from a set of local descriptors and output a single description.

Year	Paper	link	Code
[CVPR13]	All about VLAD	PDF	-
[ECCV10]	Improving the fisher kernel for large-scale image classification	PDF	-
[CVPR07]	Object retrieval with large vocabularies and fast spatial matching	PDF	-
[CVPR06]	Fisher kenrels on visual vocabularies for image categorizaton	PDF	-

• Deep learning

Similar idea but use deep learning to adapt classical algorithm

Year	Paper	link	Code
[ECCV16]	CNN Image RetrievalLearns from BoW: Unsupervised Fine-Tuning with Hard Examples.	PDF	-
[CVPR16]	NetVLAD: CNN architecture for weakly supervised place recognition	Page	Github

2. Real-valued descriptor

One single representation from the image.

Year	Paper	link	Code
[ICCV19]	Learning with Average Precision: Training Image Retrieval with a Listwise Loss	arXiv	Github
[CVPR19]	Detect-to-Retrieve: Efficient Regional Aggregation for Image Search	PDF	Github
[TPAMI18]	Fine-tuning CNN Image Retrieval with No Human Annotation	arXiv	Github
[IJCV17]	End-to-end Learning of Deep Visual Representations for Image Retrieval	arXiv	Github
[ICCV17]	Large-Scale Image Retrieval with Attentive Deep Local Features	-	Github
[ECCV16]	CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples	arXiv	Github

3. Binary descriptor and quantization

For more compact representation, a binary descriptor can be generated from hashing or thresholding. Quantization is also very popular in large-scale image retrieval.

Year	Paper	link	Code
[ICCVW19]	DAME WEB: DynAmic MEan with Whitening Ensemble Binarization for Landmark Retrieval without Human Annotation	PDF	-
[AAAI18]	Deep Region Hashing for Generic Instance Search from Image	-	-
[TPAMI18]	Supervised Learning of Semantics-Preserving Hash via Deep Convolutional NeuralNetworks	-	-
[TPAMI13]	Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval	PDF	-
[TPAMI10]	Product quantization for nearest neighbor search	PDF	-

5. Post-processing

Anything can boost the performance in the post-processing stage such as re-ranking or query expansion.

Year	Paper	link	Code
[CVPR19]	Local features and visual words emerge in activations	PDF	-
[CVPR12]	Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking	PDF	-

Multi-tasking local and global descriptors

Some works try to cover both local descriptor and global retrieval due to the shared similarity about the activation and the applications.

Year	Paper	link	Code
[CVPR19]	ContextDesc: Local Descriptor Augmentation with Cross-Modality Context	-	Github
[CVPR19]	From Coarse to Fine: Robust Hierarchical Localization at Large Scale with HF-Net	arXiv	Github
[ICCV17]	Large-Scale Image Retrieval with Attentive Deep Local Features	-	Github

Benchmarks

Local matching

Year	Paper	link	Code	Note
[CVPR17]	HPatches: A benchmark and evaluation of handcrafted and learned local descriptors	arXiv	Github	Hpatches
[TPAMI11]	Discriminative learning of local image descriptors	Page	-	UBC/Brown dataset (subsets:Liberty (New York), Notre Dame (Paris) and Half Dome (Yosemite))
[CVPR08]	On Benchmarking Camera Calibration and MultiView Stereo for High Resolution Imagery

Global retrieval

Year	Paper	link	Code	Note
[CVPR18]	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking	Page	Github	ROxford5k, RParis6k
[CVPR07]	Object retrieval with large vocabularies and fast spatial matching	Page	-	Oxford5k
[CVPR08]	Lost in Quantization: Improving Particular Object Retrieval in Large Scale Image Databases	Page	-	Paris6k

Localization (both local matching and global retrieval)

Year	Paper	link	Code	Note
[CVPR18]	Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions	PDF,Page	Github	Aachen-day-night, Robotcar, CMU-seasons