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 5160 of 477 papers

TitleStatusHype
Multi Player Tracking in Ice Hockey with Homographic Projections0
OAEI Machine Learning Dataset for Online Model GenerationCode2
Learning Latent Partial Matchings with Gumbel-IPF NetworksCode0
Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic SimilarityCode0
SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream TasksCode1
Semi-Supervised Image Captioning Considering Wasserstein Graph Matching0
Cross-modal Retrieval with Noisy Correspondence via Consistency Refining and MiningCode1
DSGG: Dense Relation Transformer for an End-to-end Scene Graph GenerationCode0
GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection0
Extreme Point Pursuit -- Part II: Further Error Bound Analysis and Applications0
Show:102550
← PrevPage 6 of 48Next →

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