Papers Graph Matching
“Graph Matching” 태그가 달린 논문 515편 · 필터 해제
Contrastive General Graph Matching with Adaptive Augmentation Sampling
Graph matching has important applications in pattern recognition and beyond. Current approaches predominantly adopt supervised learning, demanding extensive labeled data which can be limited or costly. Meanwhile, self-su…
Contrastive LearningGraph MatchingSelf-Supervised LearningGeodesic Distance Between Graphs: A Spectral Metric for Assessing the Stability of Graph Neural Networks
This paper presents a spectral framework for assessing the generalization and stability of Graph Neural Networks (GNNs) by introducing a Graph Geodesic Distance (GGD) metric. For two different graphs with the same number…
Graph MatchingA Unified Framework for Integer Programming Formulation of Graph Matching Problems
Graph theory has been a powerful tool in solving difficult and complex problems arising in all disciplines. In particular, graph matching is a classical problem in pattern analysis with enormous applications. Many graph …
Graph MatchingNetwork two-sample test for block models
We consider the two-sample testing problem for networks, where the goal is to determine whether two sets of networks originated from the same stochastic model. Assuming no vertex correspondence and allowing for different…
Graph MatchingOverall - TestStochastic Block ModelTwo-sample testingSemantic Similarity Score for Measuring Visual Similarity at Semantic Level
Semantic communication, as a revolutionary communication architecture, is considered a promising novel communication paradigm. Unlike traditional symbol-based error-free communication systems, semantic-based visual commu…
Graph GenerationGraph MatchingMS-SSIMScene Graph Generation+4ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection
In the field of 3D object detection tasks, fusing heterogeneous features from LiDAR and camera sensors into a unified Bird's Eye View (BEV) representation is a widely adopted paradigm. However, existing methods are often…
3D Object DetectionContrastive LearningDepth EstimationGraph Matching+2Network Interdiction Goes Neural
Network interdiction problems are combinatorial optimization problems involving two players: one aims to solve an optimization problem on a network, while the other seeks to modify the network to thwart the first player'…
Combinatorial OptimizationGraph MatchingTraveling Salesman ProblemDifferentiable Proximal Graph Matching
Graph matching is a fundamental tool in computer vision and pattern recognition. In this paper, we introduce an algorithm for graph matching based on the proximal operator, referred to as differentiable proximal graph ma…
Graph MatchingMulti Player Tracking in Ice Hockey with Homographic Projections
Multi Object Tracking (MOT) in ice hockey pursues the combined task of localizing and associating players across a given sequence to maintain their identities. Tracking players from monocular broadcast feeds is an import…
Graph MatchingMulti-Object TrackingObject TrackingOAEI Machine Learning Dataset for Online Model Generation
Ontology and knowledge graph matching systems are evaluated annually by the Ontology Alignment Evaluation Initiative (OAEI). More and more systems use machine learning-based approaches, including large language models. T…
Graph MatchingmodelLearning Latent Partial Matchings with Gumbel-IPF Networks
Learning to match discrete objects has been a central task in machine learning, often facilitated by a continuous relaxation of the matching structure. However, practical problems entail partial matchings due to missing …
Graph MatchingRematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity
Knowledge graphs play a pivotal role in various applications, such as question-answering and fact-checking. Abstract Meaning Representation (AMR) represents text as knowledge graphs. Evaluating the quality of these graph…
Abstract Meaning RepresentationFact CheckingGraph MatchingKnowledge Graphs+5SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks
Scene graphs have been recently introduced into 3D spatial understanding as a comprehensive representation of the scene. The alignment between 3D scene graphs is the first step of many downstream tasks such as scene grap…
3D Scene Graph AlignmentGraph MatchingGraph Neural NetworkPoint Cloud Registration+1Semi-Supervised Image Captioning Considering Wasserstein Graph Matching
Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. Existing approaches are mostly supervised…
Data AugmentationGraph MatchingImage CaptioningSentenceCross-modal Retrieval with Noisy Correspondence via Consistency Refining and Mining
The success of existing cross-modal retrieval (CMR) methods heavily rely on the assumption that the annotated cross-modal correspondence is faultless. In practice, however, the correspondence of some pairs would be inevi…
Cross-Modal RetrievalCross-modal retrieval with noisy correspondenceGraph MatchingRetrievalDSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation
Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image, which is challenging due to incomplete labelling, long-tailed relationship categories, and relational semant…
Graph GenerationGraph MatchingPanoptic Scene Graph GenerationRelation+2GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection
Integrating LiDAR and camera information into Bird's-Eye-View (BEV) representation has emerged as a crucial aspect of 3D object detection in autonomous driving. However, existing methods are susceptible to the inaccurate…
3D Object DetectionAutonomous DrivingDepth EstimationGraph Matching+2Ensemble Quadratic Assignment Network for Graph Matching
Graph matching is a commonly used technique in computer vision and pattern recognition. Recent data-driven approaches have improved the graph matching accuracy remarkably, whereas some traditional algorithm-based methods…
3D Shape ClassificationGPUGraph MatchingGraph Neural NetworkBigraph Matching Weighted with Learnt Incentive Function for Multi-Robot Task Allocation
Most real-world Multi-Robot Task Allocation (MRTA) problems require fast and efficient decision-making, which is often achieved using heuristics-aided methods such as genetic algorithms, auction-based methods, and bipart…
Decision MakingGraph MatchingExtreme Point Pursuit -- Part II: Further Error Bound Analysis and Applications
In the first part of this study, a convex-constrained penalized formulation was studied for a class of constant modulus (CM) problems. In particular, the error bound techniques were shown to play a vital role in providin…
Constrained ClusteringGraph Matching