Proceedings ArticleDOI
A Short Survey of Recent Advances in Graph Matching
Junchi Yan,Xu-Cheng Yin,Weiyao Lin,Cheng Deng,Hongyuan Zha,Xiaokang Yang +5 more
- pp 167-174
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TLDR
The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.Abstract:
Graph matching, which refers to a class of computational problems of finding an optimal correspondence between the vertices of graphs to minimize (maximize) their node and edge disagreements (affinities), is a fundamental problem in computer science and relates to many areas such as combinatorics, pattern recognition, multimedia and computer vision. Compared with the exact graph (sub)isomorphism often considered in a theoretical setting, inexact weighted graph matching receives more attentions due to its flexibility and practical utility. A short review of the recent research activity concerning (inexact) weighted graph matching is presented, detailing the methodologies, formulations, and algorithms. It highlights the methods under several key bullets, e.g. how many graphs are involved, how the affinity is modeled, how the problem order is explored, and how the matching procedure is conducted etc. Moreover, the research activity at the forefront of graph matching applications especially in computer vision, multimedia and machine learning is reported. The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.read more
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Journal ArticleDOI
A Survey of Unsupervised Deep Domain Adaptation
Garrett Wilson,Diane J. Cook +1 more
TL;DR: A survey will compare single-source and typically homogeneous unsupervised deep domain adaptation approaches, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially costly target data labels.
Journal ArticleDOI
Image Matching from Handcrafted to Deep Features: A Survey
TL;DR: This survey introduces feature detection, description, and matching techniques from handcrafted methods to trainable ones and provides an analysis of the development of these methods in theory and practice, and briefly introduces several typical image matching-based applications.
Proceedings ArticleDOI
Deep Closest Point: Learning Representations for Point Cloud Registration
Yue Wang,Justin Solomon +1 more
TL;DR: This work proposes a learning-based method, titled Deep Closest Point (DCP), inspired by recent techniques in computer vision and natural language processing, that provides a state-of-the-art registration technique and evaluates the suitability of the learned features transferred to unseen objects.
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Deep Closest Point: Learning Representations for Point Cloud Registration
Yue Wang,Justin Solomon +1 more
TL;DR: Deep Closest Point (DCP) as discussed by the authors is a learning-based method for point cloud registration, which consists of three parts: a point cloud embedding network, an attention-based module combined with a pointer generation layer to approximate combinatorial matching, and a differentiable singular value decomposition (SVD) layer to extract the final rigid transformation.
Journal ArticleDOI
Foundations of Modern Query Languages for Graph Databases
TL;DR: In this paper, the authors present a survey of the fundamental graph querying functionalities, such as graph patterns and navigational expressions, which are used in modern graph query languages such as SPARQL, Cypher and Gremlin.
References
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Journal ArticleDOI
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