Graph Matching
140 papers with code • 7 benchmarks • 11 datasets
Graph Matching is the problem of finding correspondences between two sets of vertices while preserving complex relational information among them. Since the graph structure has a strong capacity to represent objects and robustness to severe deformation and outliers, it is frequently adopted to formulate various correspondence problems in the field of computer vision. Theoretically, the Graph Matching problem can be solved by exhaustively searching the entire solution space. However, this approach is infeasible in practice because the solution space expands exponentially as the size of input data increases. For that reason, previous studies have attempted to solve the problem by using various approximation techniques.
Source: Consistent Multiple Graph Matching with Multi-layer Random Walks Synchronization
Benchmarks
These leaderboards are used to track progress in Graph Matching
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Libraries
Use these libraries to find Graph Matching models and implementationsDatasets
CUB-200-2011
PASCAL VOC
Occluded REID
SPair-71k
BirdSong
Mindboggle
IMCPT-SparseGM-50
Latest papers with no code
Exploring Naive Bayes Classifiers for Tabular Data to Knowledge Graph Matching
The present research investigates the use of Naive Bayes classifiers to match knowledge graphs and tabular data, with particular emphasis on Column Type Annotation, Cell Entity Annotation, Column Property Annotation and Table Topic Detection.
Rethinking and Benchmarking Predict-then-Optimize Paradigm for Combinatorial Optimization Problems
Numerous web applications rely on solving combinatorial optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks.
OLaLa: Ontology Matching with Large Language Models
Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process.
Differentially Private Pre-Trained Model Fusion using Decentralized Federated Graph Matching
Model fusion is becoming a crucial component in the context of model-as-a-service scenarios, enabling the delivery of high-quality model services to local users.
Graph Matching via convex relaxation to the simplex
We use this condition to show exact one-step recovery of the ground truth (holding almost surely) via the mirror descent scheme, in the noiseless setting.
M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering
Existing graph matching methods typically assume that there are similar structures between graphs and they are matchable.
Robust Graph Matching Using An Unbalanced Hierarchical Optimal Transport Framework
Given two graphs, we align their node embeddings within the same modality and across different modalities, respectively.
GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection
Through the projection calibration between the image and point cloud, we project the nearest neighbors of point cloud features onto the image features.
Clustering-based Image-Text Graph Matching for Domain Generalization
However, they use pivot embedding in global manner (i. e., aligning an image embedding with sentence-level text embedding), not fully utilizing the semantic cues of given text description.
AGMDT: Virtual Staining of Renal Histology Images with Adjacency-Guided Multi-Domain Transfer
Renal pathology, as the gold standard of kidney disease diagnosis, requires doctors to analyze a series of tissue slices stained by H&E staining and special staining like Masson, PASM, and PAS, respectively.
