超图
匹配(统计)
人工神经网络
计算机科学
节点(物理)
特征(语言学)
构造(python库)
封面(代数)
理论计算机科学
模式识别(心理学)
人工智能
数据挖掘
数学
组合数学
工程类
哲学
程序设计语言
语言学
统计
机械工程
结构工程
作者
Xiaowei Liao,Yong Xu,Haibin Ling
标识
DOI:10.1109/iccv48922.2021.00130
摘要
Hypergraph matching is a useful tool to find feature correspondence by considering higher-order structural information. Recently, the employment of deep learning has made great progress in the matching of graphs, suggesting its potential for hypergraphs. Hence, in this paper, we present the first, to our best knowledge, unified hypergraph neural network (HNN) solution for hypergraph matching. Specifically, given two hypergraphs to be matched, we first construct an association hypergraph over them and convert the hypergraph matching problem into a node classification problem on the association hypergraph. Then, we design a novel hypergraph neural network to effectively solve the node classification problem. Being end-to-end trainable, our proposed method, named HNN-HM, jointly learns all its components with improved optimization. For evaluation, HNN-HM is tested on various benchmarks and shows a clear advantage over state-of-the-arts.
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