2019年3月31日 · In this paper, we consider modeling the image-level relations to generate more informative image representations, and propose ImageGCN, an end-to-end graph …

在应用图卷积网络(Graph Convolutional Networks, GCN)到信号模式识别领域中，最关键的是如何构建数据的图结构；我们利用基于K-NN的轴承故障信号预处理的方法，来构建故障信号序列 …

2020年11月24日 · Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation between adjacent land covers and conduct …

提出了 Graph-FCN 来解决语义分割问题。 首次用 GCN 方法解决图像语义分割任务。 Graph-FCN 可以扩大感受野，同时避免局部位置信息出现损失。 实验表明，Graph-FCN 的性能优于 FCN …

2019年4月7日 · To capture and explore such important dependencies, we propose a multi-label classification model based on Graph Convolutional Network (GCN). The model builds a …

Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation between adjacent land covers and conduct flexible convolution …

mation, we propose ImageGCN, an end-to-end GCN framework on ImageGraph, to learn the image representations. In ImageGCN, each image updates the information based on its own …

PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019. In our original conference paper, we report the baseline classification results …

2020年8月18日 · Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …

2025年3月6日 · Therefore, this paper proposes a hybrid CNN-GCN network (HCGN) for hyperspectral image classification. Firstly, a graph edge enhanced module (GEEM) is …