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 1–10 of 477 papers

TitleStatusHype
Probing Neural Topology of Large Language ModelsCode0
PackHero: A Scalable Graph-based Approach for Efficient Packer IdentificationCode0
Learning without Isolation: Pathway Protection for Continual LearningCode0
Improving Chemical Understanding of LLMs via SMILES Parsing—0
Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR—0
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise RewardCode3
Tempo: Application-aware LLM Serving with Mixed SLO Requirements—0
Graph Network Modeling Techniques for Visualizing Human Mobility Patterns—0
Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation—0
DiffGED: Computing Graph Edit Distance via Diffusion-based Graph Matching—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SmatchSpearman Correlation96.57—Unverified
2RematchSpearman Correlation95.32—Unverified
3SemBleuSpearman Correlation94.83—Unverified
4S2matchSpearman Correlation94.11—Unverified
5WLKSpearman Correlation90.39—Unverified