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Graph Matching

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하위 태스크: 0
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보존된 평가 기록: 83

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

논문

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Graph Matching

논문

Optimizing Incomplete, Large-Scale and Sparse Multi-Graph Matching in Bioimaging

Probing Neural Topology of Large Language Models

PackHero: A Scalable Graph-based Approach for Efficient Packer Identification

Learning without Isolation: Pathway Protection for Continual Learning

Improving Chemical Understanding of LLMs via SMILES Parsing

Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward

Tempo: Application-aware LLM Serving with Mixed SLO Requirements

Graph Network Modeling Techniques for Visualizing Human Mobility Patterns

Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation

DiffGED: Computing Graph Edit Distance via Diffusion-based Graph Matching

Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation

UniGoal: Towards Universal Zero-shot Goal-oriented Navigation

HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity

Fully Unsupervised Annotation of C. Elegans

Robust detection of overlapping bioacoustic sound events

Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning

Snoopy: Effective and Efficient Semantic Join Discovery via Proxy Columns

Complex Ontology Matching with Large Language Model Embeddings

Exact Matching in Correlated Networks with Node Attributes for Improved Community Recovery

Robust random graph matching in Gaussian models via vector approximate message passing

From communities to interpretable network and word embedding: an unified approach

Efficient Graph Matching for Correlated Stochastic Block Models

Harnessing Multiple Correlated Networks for Exact Community Recovery

Visual-Semantic Graph Matching Net for Zero-Shot Learning

TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understanding

SEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning

Learning Partial Graph Matching via Optimal Partial Transport

CLAP: Concave Linear APproximation for Quadratic Graph Matching

CLIP-Clique: Graph-based Correspondence Matching Augmented by Vision Language Models for Object-based Global Localization

Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis

GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent

Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching

Contactless Fingerprint Recognition Using 3D Graph Matching

Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation

Cross-domain Named Entity Recognition via Graph Matching