Papers Graph Matching
“Graph Matching” 태그가 달린 논문 515편 · 필터 해제
Epipolar-Guided Deep Object Matching for Scene Change Detection
This paper describes a viewpoint-robust object-based change detection network (OBJ-CDNet). Mobile cameras such as drive recorders capture images from different viewpoints each time due to differences in camera trajectory…
Change DetectionGraph MatchingObjectScene Change DetectionFLOT: Scene Flow on Point Clouds Guided by Optimal Transport
We propose and study a method called FLOT that estimates scene flow on point clouds. We start the design of FLOT by noticing that scene flow estimation on point clouds reduces to estimating a permutation matrix in a perf…
Graph MatchingScene Flow EstimationGMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild
The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in the scene. This problem remains nowadays …
Graph MatchingObjectSemantic SegmentationMultilevel Graph Matching Networks for Deep Graph Similarity Learning
While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on gra…
Graph ClassificationGraph MatchingGraph Neural NetworkGraph Regression+2Neural Graph Matching Networks for Chinese Short Text Matching
Chinese short text matching usually employs word sequences rather than character sequences to get better performance. However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hur…
Chinese Word SegmentationGraph MatchingSegmentationSentence+1Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach.
Graph matching aims to establish node correspondence between two graphs, which has been a fundamental problem for its NP-complete nature. One practical consideration is the effective modeling of the affinity function in …
Graph MatchingGraph Optimal Transport for Cross-Domain Alignment
Cross-domain alignment between two sets of entities (e.g., objects in an image, words in a sentence) is fundamental to both computer vision and natural language processing. Existing methods mainly focus on designing adva…
Graph MatchingImage CaptioningImage-text RetrievalMachine Translation+8Determinant Regularization for Gradient-Efficient Graph Matching
Graph matching refers to finding vertex correspondence for a pair of graphs, which plays a fundamental role in many vision and learning related tasks. Directly applying gradient-based continuous optimization on graph mat…
Graph MatchingLearning Combinatorial Solver for Graph Matching
Learning-based approaches to graph matching have been developed and explored for more than a decade, have grown rapidly in scope and popularity in recent years. However, previous learning-based algorithms, with or withou…
Combinatorial OptimizationGraph MatchingNode ClassificationVisual-Semantic Matching by Exploring High-Order Attention and Distraction
Cross-modality semantic matching is a vital task in computer vision and has attracted increasing attention in recent years. Existing methods mainly explore object-based alignment between image objects and text words. In …
AttributeGraph AttentionGraph MatchingGraph Similarity+5Graph-based Proprioceptive Localization Using a Discrete Heading-Length Feature Sequence Matching Approach
Proprioceptive localization refers to a new class of robot egocentric localization methods that do not rely on the perception and recognition of external landmarks. These methods are naturally immune to bad weather, poor…
Graph MatchingGraph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation
Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, such as finding the most similar chemical compounds similar to a query compound or Fewshot…
3D Action RecognitionAction RecognitionGraph MatchingGraph Neural Network+2Graph Matching with Partially-Correct Seeds
Graph matching aims to find the latent vertex correspondence between two edge-correlated graphs and has found numerous applications across different fields. In this paper, we study a seeded graph matching problem, which …
Graph MatchingPose-guided Visible Part Matching for Occluded Person ReID
Occluded person re-identification is a challenging task as the appearance varies substantially with various obstacles, especially in the crowd scenario. To address this issue, we propose a Pose-guided Visible Part Matchi…
Graph MatchingOccluded Person Re-IdentificationPerson Re-IdentificationZero-Assignment Constraint for Graph Matching with Outliers
Graph matching (GM), as a longstanding problem in computer vision and pattern recognition, still suffers from numerous cluttered outliers in practical applications. To address this issue, we present the zero-assignment c…
Graph MatchingvalidDeep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Usi…
Combinatorial OptimizationDeep LearningGraph MatchingHigh-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
Occluded person re-identification (ReID) aims to match occluded person images to holistic ones across dis-joint cameras. In this paper, we propose a novel framework by learning high-order relation and topology informatio…
Graph MatchingOccluded Person Re-IdentificationPerson Re-IdentificationRelationGraph Deconvolutional Generation
Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering. In this work, we focus on the modern equivalent of the Erdos-Renyi random graph model: the graph…
DecoderGraph GenerationGraph 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 NetworkLanguage ModellingNatural Language Visual Grounding+2Graph Neural Distance Metric Learning with Graph-Bert
Graph distance metric learning serves as the foundation for many graph learning problems, e.g., graph clustering, graph classification and graph matching. Existing research works on graph distance metric (or graph kernel…
ClusteringGraph ClassificationGraph ClusteringGraph Learning+3