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 381–390 of 477 papers

TitleStatusHype
Contrastive General Graph Matching with Adaptive Augmentation Sampling—0
Convex Joint Graph Matching and Clustering via Semidefinite Relaxations—0
Convex Two-Layer Modeling with Latent Structure—0
Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network—0
Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities—0
CPN-CORE: A Text Semantic Similarity System Infused with Opinion Knowledge—0
Cross-domain Named Entity Recognition via Graph Matching—0
Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR—0
CURSOR: Scalable Mixed-Order Hypergraph Matching with CUR Decomposition—0
Decentralized Task Allocation in Multi-Robot Systems via Bipartite Graph Matching Augmented with Fuzzy Clustering—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