Image Processing GNN: Breaking Rigidity in Super-Resolution Yuchuan Tian1, Hanting Chen2, Chao Xu1, Yunhe Wang2∗ 1 National Key Lab of General AI, School of Intelligence Science and Technology, Peking University. 2 Huawei Noah's Ark Lab. tianyc@stu.pku.edu.cn, y...
通过将图像视为图形数据,我们探索了如何利用GNN来提取其表示。 图表示图像的优点包括:1)图是一种广义的数据结构,网格和序列可以看作图的特例;2) 图形比网格或序列更灵活地对复杂对象进行建模,因为图像中的对象通常不是形状不规则的四边形;3) 物体可以被视为多个部分的组成(例如,人可以大致分为头部、上身、手臂和...
A graph neural network (GNN) is a connectivity model that captures graph dependencies through messaging between nodes of a graph. After a systematic study of the graph neural network and its application in image processing, this paper presents the mainstream structure model for image classification ...
Image modeling is the process of creating a representation of colors and structures within an image to facilitate image processing and analysis in computer science. AI generated definition based on: Application of Machine Learning in Agriculture, 2022 ...
如图所示,每一个ViG块都由图处理(Graph Processing)和特征变换(Feature Transform)组成,对于图处理部分,和使用图神经网络处理一般的图数据一致,都是聚合(Aggregate)特征和更新(Update)特征这两个操作。作者在这里提出了多头更新(Multi-head Update),即将聚合的特征分为h个头,分别更新并串联。这样保证了特征的多样性。
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Graph Neural Networks (GNN): Recently there has been a growing interest [16, 30, 26] to extend the deep learning approaches for the graph-related data. The con- ventional CNNs can be seen as a special case of graph data in which each spatial pixel is connected by its surrounding ...
本文提出将图像表示为图(Graph)结构,并引入一种新的视觉图卷积 (Vision GNN,ViG)体系结构来提取视觉任务的图级(Graph-Level)特征。首先,将图像分割为多个块(patch),这些块被视为节点,并通过连接最近的邻居节点来构建一个图。基于图像的图表示(graph representation),通过建立ViG模型来实现所有节点之间的信息变换和...
deep learning; few-shot learning (FSL); fine-grained image classification; graph neural network (GNN)1. Introduction FSL could be considered as deep learning with few labeled samples. Traditional deep neural networks (DNN) usually require a large number of high-quality training samples without ...