Papers Graph Matching
“Graph Matching” 태그가 달린 논문 515편 · 필터 해제
GLSearch: Maximum Common Subgraph Detection via Learning to Search
Detecting the Maximum Common Subgraph (MCS) between two input graphs is fundamental for applications in drug synthesis, malware detection, cloud computing, etc. However, MCS computation is NP-hard, and state-of-the-art M…
Cloud ComputingGraph EmbeddingGraph MatchingGraph Neural Network+5Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question
Graph matching consists of aligning the vertices of two unlabeled graphs in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly formulated as minimizing their edge dis…
Graph MatchingFast and Robust Comparison of Probability Measures in Heterogeneous Spaces
Comparing two probability measures supported on heterogeneous spaces is an increasingly important problem in machine learning. Such problems arise when comparing for instance two populations of biological cells, each des…
Graph MatchingWord EmbeddingsAMR Similarity Metrics from Principles
Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013) aligns the variables of two graphs and assesses triple matches. The recent …
Abstract Meaning RepresentationComputational EfficiencyGraph MatchingMachine Translation+1Deep Graph Matching Consensus
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial rank…
Entity AlignmentGraph MatchingGraph Neural NetworkKnowledge Graphs+1Fabricated Pictures Detection with Graph Matching
Fabricating experimental pictures in research work is a serious academic misconduct, which should better be detected in the reviewing process. However, due to large number of submissions, the detection whether a picture …
Graph MatchingLearning deep graph matching with channel-independent embedding and Hungarian attention
Graph matching aims to establishing node-wise correspondence between two graphs, which is a classic combinatorial problem and in general NP-complete. Until very recently, deep graph matching methods start to resort to de…
Graph MatchingHard AttentionSpectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
Graph matching, also known as network alignment, aims at recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs. To tackle this task, we propose a spectral method, GRAph Matchi…
Computational EfficiencyGraph MatchingAchieving Arbitrary Throughput-Fairness Trade-offs in the Inter Cell Interference Coordination with Fixed Transmit Power Problem
We study the problem of inter cell interference coordination (ICIC) with fixed transmit power in OFDMA-based cellular networks, in which each base station (BS) needs to decide as to which subchannel, if any, to allocate …
FairnessGraph MatchingElastic registration based on compliance analysis and biomechanical graph matching
An automatic elastic registration method suited for vascularized organs is proposed. The vasculature in both the preoperative and intra-operative images is represented as a graph. A typical application of this method is …
Graph MatchingDynamic Graph Representation for Partially Occluded Biometrics
The generalization ability of Convolutional neural networks (CNNs) for biometrics drops greatly due to the adverse effects of various occlusions. To this end, we propose a novel unified framework integrated the merits of…
Graph MatchingNeural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching
Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's Quadratic Assignment Problem (QAP). This paper presents a QAP network directly learn…
Combinatorial OptimizationGeneral ClassificationGraph MatchingHypergraph MatchingKerGM: Kernelized Graph Matching
Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics. Graph matching problems can be cast as two types of quadratic assignment problems (QAPs): Koopmans-Beckmann'…
Graph MatchingLearning Cross-modal Context Graph for Visual Grounding
Visual grounding is a ubiquitous building block in many vision-language tasks and yet remains challenging due to large variations in visual and linguistic features of grounding entities, strong context effect and the res…
Graph MatchingGraph Neural NetworkVisual GroundingGLMNet: Graph Learning-Matching Networks for Feature Matching
Recently, graph convolutional networks (GCNs) have shown great potential for the task of graph matching. It can integrate graph node feature embedding, node-wise affinity learning and matching optimization together in a …
Graph LearningGraph MatchingTask 2R-local unlabeled sensing: A novel graph matching approach for multiview unlabeled sensing under local permutations
Unlabeled sensing is a linear inverse problem where the measurements are scrambled under an unknown permutation leading to loss of correspondence between the measurements and the rows of the sensing matrix. Motivated by …
Graph MatchingScene Graph based Image Retrieval -- A case study on the CLEVR Dataset
With the prolification of multimodal interaction in various domains, recently there has been much interest in text based image retrieval in the computer vision community. However most of the state of the art techniques m…
Graph MatchingImage Retrievalmultimodal interactionQuestion Answering+1Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs
When samples have internal structure, we often see a mismatch between the objective optimized during training and the model's goal during inference. For example, in sequence-to-sequence modeling we are interested in high…
Combinatorial OptimizationDecoderGraph Matchingimage-classification+1Heterogeneous Graph Matching Networks
Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfu…
Graph MatchingMalware DetectionCross-modal Scene Graph Matching for Relationship-aware Image-Text Retrieval
Image-text retrieval of natural scenes has been a popular research topic. Since image and text are heterogeneous cross-modal data, one of the key challenges is how to learn comprehensive yet unified representations to ex…
Graph MatchingImage-text RetrievalRetrievalText Retrieval