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 176–200 of 477 papers

TitleStatusHype
Texture-Based Input Feature Selection for Action Recognition—0
Graph Matching Optimization Network for Point Cloud Registration—0
Drawing Attention to Detail: Pose Alignment through Self-Attention for Fine-Grained Object ClassificationCode0
Population-wise Labeling of Sulcal Graphs using Multi-graph MatchingCode0
Model-based inexact graph matching on top of CNNs for semantic scene understandingCode0
On the limits of neural network explainability via descramblingCode0
AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms—0
PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching—0
Deep Learning of Partial Graph Matching via Differentiable Top-K—0
Video Action Segmentation via Contextually Refined Temporal Keypoints—0
Editable Image Geometric Abstraction via Neural Primitive Assembly—0
CIGAR: Cross-Modality Graph Reasoning for Domain Adaptive Object Detection—0
A polynomial time iterative algorithm for matching Gaussian matrices with non-vanishing correlation—0
Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge BaseCode0
GrannGAN: Graph annotation generative adversarial networksCode0
Universe Points Representation Learning for Partial Multi-Graph Matching—0
Fast Key Points Detection and Matching for Tree-Structured Images—0
Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport—0
KGCODE-Tab Results for SemTab 2022—0
Results of SemTab 2022—0
Towards an Approach based on Knowledge Graph Refinement for Tabular Data to Knowledge Graph MatchingCode0
Privacy-Preserved Neural Graph Similarity LearningCode0
End-to-End Context-Aided Unicity Matching for Person Re-identification—0
Learning Universe Model for Partial Matching Networks over Multiple Graphs—0
QuAnt: Quantum Annealing with Learnt Couplings—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