SOTAVerified

Graph Matching

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

Papers

Showing 151–175 of 477 papers

TitleStatusHype
AGMDT: Virtual Staining of Renal Histology Images with Adjacency-Guided Multi-Domain Transfer—0
Explaining Vision and Language through Graphs of Events in Space and Time—0
Gotta match 'em all: Solution diversification in graph matching matched filters—0
An approach based on Open Research Knowledge Graph for Knowledge Acquisition from scientific papers—0
Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling—0
Shape-Graph Matching Network (SGM-net): Registration for Statistical Shape Analysis—0
Improving ICD-based semantic similarity by accounting for varying degrees of comorbidity—0
Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration—0
Learning Scene-Pedestrian Graph for End to end Person SearchCode0
Co-attention Graph Pooling for Efficient Pairwise Graph Interaction LearningCode0
Unsupervised Deep Graph Matching Based on Cycle ConsistencyCode0
Blind Graph Matching Using Graph Signals—0
LIC-GAN: Language Information Conditioned Graph Generative GAN Model—0
A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation—0
AbODE: Ab Initio Antibody Design using Conjoined ODEs—0
Efficient Algorithms for Exact Graph Matching on Correlated Stochastic Block Models with Constant CorrelationCode0
GEST: the Graph of Events in Space and Time as a Common Representation between Vision and Language—0
Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network—0
Bi-VLGM : Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation—0
Image Segmentation via Probabilistic Graph Matching—0
Semantic-Aware Graph Matching Mechanism for Multi-Label Image RecognitionCode0
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos—0
Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection—0
PATS: Patch Area Transportation with Subdivision for Local Feature Matching—0
Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GMT-BBGMmatching accuracy0.84—Unverified
2GMTRmatching accuracy0.84—Unverified
3COMMONmatching accuracy0.83—Unverified
4GCANmatching accuracy0.82—Unverified
5URLmatching accuracy0.82—Unverified
6CREAMmatching accuracy0.81—Unverified
7ASAR-GMmatching accuracy0.81—Unverified
8GAMnetmatching accuracy0.81—Unverified
9NHGM-v2matching accuracy0.8—Unverified
10EAGMmatching accuracy0.71—Unverified
#ModelMetricClaimedVerifiedStatus
1COMMONmatching accuracy0.99—Unverified
2GANN-MGMmatching accuracy0.99—Unverified
3URLmatching accuracy0.99—Unverified
4CREAMmatching accuracy0.99—Unverified
5Direct-MGMmatching accuracy0.99—Unverified
6GMT-BBGMmatching accuracy0.98—Unverified
7Direct-2HGMmatching accuracy0.98—Unverified
8qc-DGM2matching accuracy0.98—Unverified
9NGM-v2matching accuracy0.98—Unverified
10BBGMmatching accuracy0.97—Unverified
#ModelMetricClaimedVerifiedStatus
1CREAMmatching accuracy0.85—Unverified
2COMMONmatching accuracy0.85—Unverified
3GMTRmatching accuracy0.83—Unverified
4GMT-BBGMmatching accuracy0.83—Unverified
5BBGMmatching accuracy0.82—Unverified
6GCANmatching accuracy0.82—Unverified
7NGM-v2matching accuracy0.81—Unverified
8NGMmatching accuracy0.69—Unverified
#ModelMetricClaimedVerifiedStatus
1GCAN-AFAT-UF1 score0.72—Unverified
2GCAN-AFAT-IF1 score0.71—Unverified
3NGMv2-AFAT-UF1 score0.7—Unverified
4NGMv2-AFAT-IF1 score0.7—Unverified
5NGMv2F1 score0.68—Unverified
6PCA-GMF1 score0.58—Unverified
#ModelMetricClaimedVerifiedStatus
1GCAN-AFAT-IF1 score0.73—Unverified
2NGMv2-AFAT-IF1 score0.73—Unverified
3NGMv2-AFAT-UF1 score0.72—Unverified
4GCAN-AFAT-UF1 score0.71—Unverified
5NGMv2F1 score0.7—Unverified
6PCA-GMF1 score0.63—Unverified
#ModelMetricClaimedVerifiedStatus
1SmatchSpearman Correlation96.57—Unverified
2RematchSpearman Correlation95.32—Unverified
3SemBleuSpearman Correlation94.83—Unverified
4S2matchSpearman Correlation94.11—Unverified
5WLKSpearman Correlation90.39—Unverified
#ModelMetricClaimedVerifiedStatus
1URLF1 score0.95—Unverified
2GUMBEL-IPFF1 score0.84—Unverified
3IPCA-GMF1 score0.83—Unverified
4GANN-MGMF1 score0.83—Unverified