Graph Matching
138 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
Semi-Supervised Image Captioning Considering Wasserstein Graph Matching
Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features.
DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation
Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image, which is challenging due to incomplete labelling, long-tailed relationship categories, and relational semantic overlap.
GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection
Additionally, we propose a Global Align module to rectify the misalignment between LiDAR and camera BEV features.
Ensemble Quadratic Assignment Network for Graph Matching
In this paper, we propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
Bigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocation
Most real-world Multi-Robot Task Allocation (MRTA) problems require fast and efficient decision-making, which is often achieved using heuristics-aided methods such as genetic algorithms, auction-based methods, and bipartite graph matching methods.
Extreme Point Pursuit -- Part II: Further Error Bound Analysis and Applications
In the first part of this study, a convex-constrained penalized formulation was studied for a class of constant modulus (CM) problems.
CURSOR: Scalable Mixed-Order Hypergraph Matching with CUR Decomposition
To achieve greater accuracy, hypergraph matching algorithms require exponential increases in computational resources.
Multi-graph Graph Matching for Coronary Artery Semantic Labeling
However, deep learning-based methods encounter challenges in generating semantic labels for arterial segments, primarily due to the morphological similarity between arterial branches.
SAT-Based Algorithms for Regular Graph Pattern Matching
We propose a generalization of graph isomorphism that allows one to check such properties through a declarative specification.
TorchicTab: Semantic Table Annotation with Wikidata and Language Models
The Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) was introduced in an effort to benchmark semantic table interpretation systems, by evaluating them over diverse datasets and tasks.
