Graph Matching
154 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
SPair-71k
Occluded REID
BirdSong
Mindboggle
IMCPT-SparseGM-50
Most implemented papers
MolGAN: An implicit generative model for small molecular graphs
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
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
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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
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.
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
xmed-lab/GraphEcho
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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.
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Lin-Yijie/Graph-Matching-Networks
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ICLR 2019
This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions.
AMR Similarity Metrics from Principles
Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs.
Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment
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
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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
In this paper, we introduce a graph matching method that can account for constraints of arbitrary order, with arbitrary potential functions.
