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 76100 of 477 papers

TitleStatusHype
LayoutGMN: Neural Graph Matching for Structural Layout SimilarityCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object TrackingCode1
SSIG: A Visually-Guided Graph Edit Distance for Floor Plan SimilarityCode1
Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation0
Adiabatic Quantum Graph Matching with Permutation Matrix Constraints0
BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR0
Blind Graph Matching Using Graph Signals0
A Matrix Decomposition Perspective to Multiple Graph Matching0
Determinant Regularization for Gradient-Efficient Graph Matching0
Bi-VLGM : Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation0
Bipartite Graph Matching for Keyframe Summary Evaluation0
AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model0
Binary Constraint Preserving Graph Matching0
Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport0
Achieving Arbitrary Throughput-Fairness Trade-offs in the Inter Cell Interference Coordination with Fixed Transmit Power Problem0
Differentiable Proximal Graph Matching0
Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport0
A Graph Matching Perspective With Transformers on Video Instance Segmentation0
Bigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocation0
A Graph-Matching Approach for Cross-view Registration of Over-view 2 and Street-view based Point Clouds0
Dominant Z-Eigenpairs of Tensor Kronecker Products are Decoupled and Applications to Higher-Order Graph Matching0
A Weighted Common Subgraph Matching Algorithm0
AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks0
AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms0
Show:102550
← PrevPage 4 of 20Next →

Benchmark Results

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