Graph Matching
139 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
Latest papers
CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches
geods/cats
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The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users.
SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity
In this paper, an effective evaluation metric for judging the structural similarity of floor plans, coined SSIG (Structural Similarity by IoU and GED), is proposed based on both image and graph distances.
Improving ICD-based semantic similarity by accounting for varying degrees of comorbidity
The sets have been extracted from patients with a C25. X (pancreatic cancer) primary diagnosis and provide a variety of different combinations of ICD-codes.
Learning Scene-Pedestrian Graph for End to end Person Search
Vill-Lab/2023-TII-SPG
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IEEE Transactions on Industrial Informatics 2023
In this article, a novel scene-pedestrian graph (SPG) is proposed, which can explicitly model the interplay between the pedestrians and scenes.
Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning
leejunhyun/coattentiongraphpooling
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Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data.
Multiscale Dynamic Graph Representation for Biometric Recognition with Occlusions
renmin1991/dyamic-graph-representation
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Occlusion is a common problem with biometric recognition in the wild.
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
duyhominhnguyen/LVM-Med
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NeurIPS 2023
While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images.
Efficient Algorithms for Exact Graph Matching on Correlated Stochastic Block Models with Constant Correlation
We consider the problem of graph matching, or learning vertex correspondence, between two correlated stochastic block models (SBMs).
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks.
Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition
In this paper, we treat each image as a bag of instances, and formulate the task of multi-label image recognition as an instance-label matching selection problem.
