Graph Matching

132 papers with code • 6 benchmarks • 10 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

Libraries

Use these libraries to find Graph Matching models and implementations

Do Vision and Language Encoders Represent the World Similarly?

mayug/0-shot-llm-vision 10 Jan 2024

In the absence of statistical similarity in aligned encoders like CLIP, we show that a possible matching of unaligned encoders exists without any training.

1
10 Jan 2024

xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networks

martinakaduc/xneusm 4 Dec 2023

Subgraph matching is a challenging problem with a wide range of applications in database systems, biochemistry, and cognitive science.

5
04 Dec 2023

SpotServe: Serving Generative Large Language Models on Preemptible Instances

hsword/spotserve 27 Nov 2023

This paper aims to reduce the monetary cost for serving LLMs by leveraging preemptible GPU instances on modern clouds, which offer accesses to spare GPUs at a much cheaper price than regular instances but may be preempted by the cloud at any time.

41
27 Nov 2023

GMTR: Graph Matching Transformers

jp-guo/gm-transformer 14 Nov 2023

Meanwhile, on Pascal VOC, QueryTrans improves the accuracy of NGMv2 from $80. 1\%$ to $\mathbf{83. 3\%}$, and BBGM from $79. 0\%$ to $\mathbf{84. 5\%}$.

3
14 Nov 2023

UniMAP: Universal SMILES-Graph Representation Learning

fengshikun/unimap 22 Oct 2023

Molecular representation learning is fundamental for many drug related applications.

4
22 Oct 2023

PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling

seonghwanseo/pharmaconet 1 Oct 2023

Different pre-screening methods have been developed for rapid screening, but there is still a lack of structure-based methods applicable to various proteins that perform protein-ligand binding conformation prediction and scoring in an extremely short time.

23
01 Oct 2023

GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation

xmed-lab/GraphEcho ICCV 2023

This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source domain to other unlabelled target domains.

23
20 Sep 2023

CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches

geods/cats 20 Sep 2023

The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users.

16
20 Sep 2023

SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity

caspervanengelenburg/ssig 8 Sep 2023

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.

4
08 Sep 2023

Improving ICD-based semantic similarity by accounting for varying degrees of comorbidity

janschneida/taxodist 14 Aug 2023

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.

2
14 Aug 2023