Graph Matching

127 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


Use these libraries to find Graph Matching models and implementations

Most implemented papers

MolGAN: An implicit generative model for small molecular graphs

nicola-decao/MolGAN 30 May 2018

Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures.

graph2vec: Learning Distributed Representations of Graphs

benedekrozemberczki/karateclub 17 Jul 2017

Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.

Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

martius-lab/blackbox-deep-graph-matching 25 Mar 2020

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers.

Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts

clovaai/mxfont ICCV 2021

MX-Font extracts multiple style features not explicitly conditioned on component labels, but automatically by multiple experts to represent different local concepts, e. g., left-side sub-glyph.

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

Lin-Yijie/Graph-Matching-Networks ICLR 2019

This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions.

Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment

gxytcrc/Semantic-Graph-based--global-Localization 19 Oct 2020

The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL).

Graph Matching with Bi-level Noisy Correspondence

Thinklab-SJTU/ThinkMatch ICCV 2023

In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC).

Alternating Direction Graph Matching

netw0rkf10w/adgm CVPR 2017

In this paper, we introduce a graph matching method that can account for constraints of arbitrary order, with arbitrary potential functions.

Multi-Image Semantic Matching by Mining Consistent Features

zju-3dv/multiway CVPR 2018

This work proposes a multi-image matching method to estimate semantic correspondences across multiple images.

Latent Fingerprint Recognition: Role of Texture Template

prip-lab/MSU-LatentAFIS 27 Apr 2018

We propose a texture template approach, consisting of a set of virtual minutiae, to improve the overall latent fingerprint recognition accuracy.