Papers Graph Matching
“Graph Matching” 태그가 달린 논문 515편 · 필터 해제
Learning Combinatorial Embedding Networks for Deep Graph Matching
Graph matching refers to finding node correspondence between graphs, such that the corresponding node and edge's affinity can be maximized. In addition with its NP-completeness nature, another important challenge is effe…
Graph EmbeddingGraph MatchingAn Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing
Graph matching is one of the most important problems in graph theory and combinatorial optimization, with many applications in various domains. Although meta-heuristic algorithms have had good performance on many NP-Hard…
Combinatorial OptimizationGraph MatchingStochastic OptimizationVisual Query Answering by Entity-Attribute Graph Matching and Reasoning
Visual Query Answering (VQA) is of great significance in offering people convenience: one can raise a question for details of objects, or high-level understanding about the scene, over an image. This paper proposes a nov…
AttributeGraph MatchingVisual Question Answering (VQA)Drug-drug interaction prediction based on co-medication patterns and graph matching
Background: The problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered in this manuscript. Methods: Novel kernels over drug combinations of arbitrary…
Graph MatchingPredictionA General Theory for Structured Prediction with Smooth Convex Surrogates
In this work we provide a theoretical framework for structured prediction that generalizes the existing theory of surrogate methods for binary and multiclass classification based on estimating conditional probabilities w…
General ClassificationGraph MatchingregressionStructured PredictionComputing Optimal Assignments in Linear Time for Approximate Graph Matching
Finding an optimal assignment between two sets of objects is a fundamental problem arising in many applications, including the matching of `bag-of-words' representations in natural language processing and computer vision…
Graph MatchingDeep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
A graph is a powerful concept for representation of relations between pairs of entities. Data with underlying graph structure can be found across many disciplines and there is a natural desire for understanding such data…
Graph MatchingAutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks
ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its basic operations include group, channel-wise convolution and channel shuffling. However, channel shuffling is manually designed…
Graph MatchingGromov-Wasserstein Learning for Graph Matching and Node Embedding
A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes. Using Gromov-Wasserstein discrepancy, we measure the dissimilarity bet…
Graph MatchingA Functional Representation for Graph Matching
Graph matching is an important and persistent problem in computer vision and pattern recognition for finding node-to-node correspondence between graph-structured data. However, as widely used, graph matching that incorpo…
Graph MatchingGM-PLL: Graph Matching based Partial Label Learning
Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct. The key to deal with such problem is to disambiguate the…
Graph MatchingPartial Label LearningMaximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks
Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagr…
Graph MatchingGeneralizing Graph Matching beyond Quadratic Assignment Model
Graph matching has received persistent attention over decades, which can be formulated as a quadratic assignment problem (QAP). We show that a large family of functions, which we define as Separable Functions, can approx…
Graph MatchingmodelThe Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown)…
Binary ClassificationClassificationGeneral ClassificationGraph MatchingMatching Disparate Image Pairs Using Shape-Aware ConvNets
An end-to-end trainable ConvNet architecture, that learns to harness the power of shape representation for matching disparate image pairs, is proposed. Disparate image pairs are deemed those that exhibit strong affine va…
Graph MatchingMatching Disparate ImagesEfficient random graph matching via degree profiles
Random graph matching refers to recovering the underlying vertex correspondence between two random graphs with correlated edges; a prominent example is when the two random graphs are given by Erd\H{o}s-R\'{e}nyi graphs $…
Graph Matching3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair
The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for need of 3D alignments between inserted devices and aortic branches. The navigation commonly via 2D fluoro…
Computed Tomography (CT)Graph MatchingImage to Point Cloud RegistrationElastic Registration of Geodesic Vascular Graphs
Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sectional clinical inference. This, however,…
Graph MatchingSample Complexity of Nonparametric Semi-Supervised Learning
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown)…
Binary ClassificationClassificationGeneral ClassificationGraph MatchingLearning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity …
ClusteringCombinatorial OptimizationGraph ClassificationGraph Clustering+3