Papers Graph Matching
“Graph Matching” 태그가 달린 논문 515편 · 필터 해제
Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?
Graph matching is an important problem that has received widespread attention, especially in the field of computer vision. Recently, state-of-the-art methods seek to incorporate graph matching with deep learning. However…
Graph MatchingCorrelated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities
We consider the task of learning latent community structure from multiple correlated networks. First, we study the problem of learning the latent vertex correspondence between two edge-correlated stochastic block models,…
Graph MatchingAdiabatic Quantum Graph Matching with Permutation Matrix Constraints
Matching problems on 3D shapes and images are challenging as they are frequently formulated as combinatorial quadratic assignment problems (QAPs) with permutation matrix constraints, which are NP-hard. In this work, we a…
Graph Matchingvalid3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching
We address the problem of 3D shape registration and we propose a novel technique based on spectral graph theory and probabilistic matching. The task of 3D shape analysis involves tracking, recognition, registration, etc.…
Dimensionality ReductionGraph EmbeddingGraph MatchingMulti-Modal Relational Graph for Cross-Modal Video Moment Retrieval
Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically…
Cross-Modal RetrievalGraph MatchingMoment RetrievalRelation+2G2DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification
RGB-Infrared (IR) person re-identification aims to retrieve person-of-interest from heterogeneous cameras, easily suffering from large image modality discrepancy caused by different sensing wavelength ranges. Existing wo…
graph constructionGraph LearningGraph MatchingImage Retrieval+2A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval
Conventional approaches to image-text retrieval mainly focus on indexing visual objects appearing in pictures but ignore the interactions between these objects. Such objects occurrences and interactions are equivalently …
Graph MatchingImage RetrievalImage-text matchingImage-text Retrieval+4Stochastic Iterative Graph Matching
Recent works leveraging Graph Neural Networks to approach graph matching tasks have shown promising results. Recent progress in learning discrete distributions poses new opportunities for learning graph matching models. …
Graph MatchingStochastic OptimizationIA-GM: A Deep Bidirectional Learning Method for Graph Matching
Existing deep learning methods for graph matching(GM) problems usually considered affinity learningto assist combinatorial optimization in a feedforward pipeline, and parameter learning is executed by back-propagating th…
Combinatorial OptimizationGraph MatchingNeural Graph Matching based Collaborative Filtering
User and item attributes are essential side-information; their interactions (i.e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems. We identify two dif…
AttributeCollaborative FilteringGraph LearningGraph Matching+1Pairwise Point Cloud Registration using Graph Matching and Rotation-invariant Features
Registration is a fundamental but critical task in point cloud processing, which usually depends on finding element correspondence from two point clouds. However, the finding of reliable correspondence relies on establis…
Graph MatchingPoint Cloud RegistrationTranslationVersaGNN: a Versatile accelerator for Graph neural networks
\textit{Graph Neural Network} (GNN) is a promising approach for analyzing graph-structured data that tactfully captures their dependency information via node-level message passing. It has achieved state-of-the-art perfor…
CPUGPUGraph GenerationGraph Matching+2GM-MLIC: Graph Matching based Multi-Label Image Classification
Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the co…
ClassificationGeneral ClassificationGraph Matchingimage-classification+2Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, such as node, link or graph classification…
ClusteringGeneral ClassificationGraph ClassificationGraph Matching+3Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning
Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks. However, the graph pooling technique for learning expressive graph-le…
Graph ClassificationGraph MatchingGraph Representation LearningGraph Similarity+1Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins
Ongoing standardization in Industry 4.0 supports tool vendor neutral representations of Piping and Instrumentation diagrams as well as 3D pipe routing. However, a complete digital plant model requires combining these two…
Graph MatchingPermutation-Invariant Subgraph Discovery
We introduce Permutation and Structured Perturbation Inference (PSPI), a new problem formulation that abstracts many graph matching tasks that arise in systems biology. PSPI can be viewed as a robust formulation of the p…
Graph MatchingMultiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
A few-shot font generation (FFG) method has to satisfy two objectives: the generated images should preserve the underlying global structure of the target character and present the diverse local reference style. Existing …
DisentanglementFont GenerationGraph MatchingGraph Matching and Graph Rewriting: GREW tools for corpus exploration, maintenance and conversion
This article presents a set of tools built around the Graph Rewriting computational framework which can be used to compute complex rule-based transformations on linguistic structures. Application of the graph matching me…
Graph MatchingJoint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections
Graph matching aims to establish correspondences between vertices of graphs such that both the node and edge attributes agree. Various learning-based methods were recently proposed for finding correspondences between ima…
3D geometryGraph Matching