Graph Matching
138 papers with code • 7 benchmarks • 11 datasets
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
Benchmarks
These leaderboards are used to track progress in Graph Matching
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Libraries
Use these libraries to find Graph Matching models and implementationsDatasets
CUB-200-2011
PASCAL VOC
Occluded REID
SPair-71k
BirdSong
Mindboggle
IMCPT-SparseGM-50
Most implemented papers
Latent Fingerprint Recognition: Role of Texture Template
We propose a texture template approach, consisting of a set of virtual minutiae, to improve the overall latent fingerprint recognition accuracy.
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
HongtengXu/gwl
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A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes.
SOSNet: Second Order Similarity Regularization for Local Descriptor Learning
Despite the fact that Second Order Similarity (SOS) has been used with significant success in tasks such as graph matching and clustering, it has not been exploited for learning local descriptors.
SemBleu: A Robust Metric for AMR Parsing Evaluation
Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs.
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
However, unlike such fields, it is hard to apply traditional deep learning models on the graph data due to the 'node-orderless' property.
MTab: Matching Tabular Data to Knowledge Graph using Probability Models
This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019).
Learning Cross-modal Context Graph for Visual Grounding
To address their limitations, this paper proposes a language-guided graph representation to capture the global context of grounding entities and their relations, and develop a cross-modal graph matching strategy for the multiple-phrase visual grounding task.
Dynamic Graph Representation for Partially Occluded Biometrics
RenMin1991/Dyamic_Graph_Representation
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During dynamic graph matching, we propose a novel strategy to measure the distances of both nodes and adjacent matrixes.
Deep Graph Matching Consensus
rusty1s/deep-graph-matching-consensus
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ICLR 2020
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
wangguanan/HOReID
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CVPR 2020
When aligning two groups of local features from two images, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to jointly learn and embed topology information to local features, and straightly predict similarity score.
