Jun 21, 2024 · Graph Convolutional Networks (GCNs) are a type of neural network designed to work directly with graphs. A graph consists of nodes (vertices) and edges (connections between nodes). In a GCN, each node represents an entity, and the edges represent the relationships between these entities.

The graph convolution network are derived from graph signal processing, and a filter is introduced to define graph convolution, which can be understood as removing noise through a filter to obtain the classification result of the input signal.

We introduce a lightweight Edge-Conditioned Convolution which addresses vanishing gradient and over-parameterization issues of this particular graph convolution. Extensive experiments show state-of-the-art performance with improved qualitative and quantitative results on both synthetic Gaussian noise and real noise.

In this paper, we propose a novel encoder-decoder network with added graph convolutions by converting feature maps to vertexes of a pre-generated graph to synthetically construct graph-structured data. By doing this, we inexplicitly apply graph Laplacian regularization to the feature maps, making them more structured.

1 day ago · It integrates insights from existing Thangka image processing studies 1,2,3,4 and adopts advanced techniques used in the ... Single-step graph convolution updates feature representations in a ...

Nov 6, 2023 · To address these issues, we propose a novel multi-level feature pyramid graph convolutional neural network that combines multi-scale adaptive atrous graph convolution (MSAGConv) and learnable graph pooling (LGP) techniques.

May 21, 2021 · In this paper, we propose a novel encoder-decoder network with added graph convolutions by converting feature maps to vertexes of a pre-generated graph to synthetically construct graph-structured data.

'Graph Convolution' refers to the operation in graph convolutional networks where feature representations of nodes and graphs are learned by passing messages through the edges of a graph, similar to how convolutional neural networks consider the spatial structure of …

2 days ago · One of the most important processes performed on hyperspectral images is their classification. In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image classification, each attempting to address the hyperspectral data's computational and processing challenges. Convolutional neural networks become less efficient at solving complex problems as the number ...

2 days ago · Sharma and Kumar 6 introduced three fundamental data-wrangling techniques to preprocess numerical tabular data from breast cancer studies for conversion into image format: an equidistant bar graph ...