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
Most implemented papers
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
Thinklab-SJTU/awesome-ml4co
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As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i. e. the number of inliers is needed to avoid matching outliers.
Robust Point Cloud Registration Framework Based on Deep Graph Matching
fukexue/RGM
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CVPR 2021
In this paper, we propose a novel deep graph matchingbased framework for point cloud registration.
Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?
C-puqing/DIP-GM
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Graph matching is an important problem that has received widespread attention, especially in the field of computer vision.
Adaptive Edge Attention for Graph Matching with Outliers
bestwei/eagm
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International Joint Conference on Artificial Intelligence 2021
To explore the potential of edges, EAGM learns edge attention on the assignment graph to 1) reveal the impact of each edge on graph matching, as well as 2) adjust the learning of edge representations adaptively.
Joint Graph Learning and Matching for Semantic Feature Correspondence
LiuHeBJTU/GLAM
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In this paper, we propose a joint \emph{graph learning and matching} network, named GLAM, to explore reliable graph structures for boosting graph matching.
Graph Neural Networks for Cross-Camera Data Association
To avoid the usage of fixed distances, we leverage the connectivity of Graph Neural Networks, previously unused in this scope, using a Message Passing Network to jointly learn features and similarity.
Backpropagation through Combinatorial Algorithms: Identity with Projection Works
martius-lab/solver-differentiation-identity
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Embedding discrete solvers as differentiable layers has given modern deep learning architectures combinatorial expressivity and discrete reasoning capabilities.
PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
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.
A Fast Projected Fixed-Point Algorithm for Large Graph Matching
In particular, with high accuracy, our algorithm takes only a few seconds (in a PC) to match two graphs of 1, 000 nodes.
