Papers Graph Matching
“Graph Matching” 태그가 달린 논문 515편 · 필터 해제
A Comparative Study of Graph Matching Algorithms in Computer Vision
The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been p…
Graph MatchingBoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR
This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified …
Graph MatchingInstance SegmentationPose EstimationSemantic SegmentationMotion Estimation for Large Displacements and Deformations
Large displacement optical flow is an integral part of many computer vision tasks. Variational optical flow techniques based on a coarse-to-fine scheme interpolate sparse matches and locally optimize an energy model cond…
Graph MatchingMotion EstimationOptical Flow EstimationSuperpixelsMuffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
Decentralized optimization is increasingly popular in machine learning for its scalability and efficiency. Intuitively, it should also provide better privacy guarantees, as nodes only observe the messages sent by their n…
Graph MatchingTemplate based Graph Neural Network with Optimal Transport Distances
Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing, and aggregation with a specialized form of pooling. The structural (or topolo…
Graph ClassificationGraph MatchingGraph Neural NetworkCGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As mo…
Collaborative FilteringGraph ClassificationGraph MatchingGraph Similarity+2Backpropagation through Combinatorial Algorithms: Identity with Projection Works
Embedding discrete solvers as differentiable layers has given modern deep learning architectures combinatorial expressivity and discrete reasoning capabilities. The derivative of these solvers is zero or undefined, there…
Density EstimationGraph MatchingImage RetrievalRetrieval+1Image Keypoint Matching using Graph Neural Networks
Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images. When images are represented as graphs, image …
Graph MatchingGraph Neural NetworkSeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching
There is a growing interest in designing Graph Neural Networks (GNNs) for seeded graph matching, which aims to match two unlabeled graphs using only topological information and a small set of seed nodes. However, most pr…
Graph MatchingClustered Graph Matching for Label Recovery and Graph Classification
Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network. We c…
ClassificationGraph ClassificationGraph MatchingLearning Constrained Structured Spaces with Application to Multi-Graph Matching
Multi-graph matching is a prominent structured prediction task, in which the predicted label is constrained to the space of cycle-consistent matchings. While direct loss minimization is an effective method for learning p…
Graph MatchingStructured PredictionKERMIT - A Transformer-Based Approach for Knowledge Graph Matching
One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions. With the rise of transformer-based language models, text comparison based on meaning (rather than l…
Graph MatchingKnowledge GraphsSentenceNeural Graph Matching for Modification Similarity Applied to Electronic Document Comparison
In this paper, we present a novel neural graph matching approach applied to document comparison. Document comparison is a common task in the legal and financial industries. In some cases, the most important differences m…
ArticlesDocument Layout AnalysisGraph MatchingSeeded graph matching for the correlated Gaussian Wigner model via the projected power method
In the \emph{graph matching} problem we observe two graphs $G,H$ and the goal is to find an assignment (or matching) between their vertices such that some measure of edge agreement is maximized. We assume in this work th…
Graph MatchingSPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors. Spectral …
Graph GenerationGraph MatchingMulti-Robot Active Mapping via Neural Bipartite Graph Matching
We study the problem of multi-robot active mapping, which aims for complete scene map construction in minimum time steps. The key to this problem lies in the goal position estimation to enable more efficient robot moveme…
Graph MatchingGraph Neural NetworkPositionreinforcement-learning+2Exact Community Recovery in Correlated Stochastic Block Models
We consider the problem of learning latent community structure from multiple correlated networks. We study edge-correlated stochastic block models with two balanced communities, focusing on the regime where the average d…
Graph MatchingSMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance
The similarity of graph structures, such as Meaning Representations (MRs), is often assessed via structural matching algorithms, such as Smatch (Cai and Knight, 2013). However, Smatch involves a combinatorial problem tha…
ClusteringData AugmentationGraph ClusteringGraph Matching+4Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization
We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-FW, for solving quadratic, linearly-constrained, binary optimization problems on quantum annealers (QA). The computational premise of …
Graph MatchingSIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection
Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn an object detector generalizing to a novel domain free of annotations. Recent advances align class-conditional distributions by narrowing down c…
Graph MatchingHallucinationobject-detectionObject Detection