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学术报告-Revisiting Graph Convolutional Networks: From Paradigm to Application（图）
学术报告 图神经网络 王石平
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2024/4/15
图神经网络（Graph Neural Network，GNN）是指使用神经网络来学习图结构数据，提取和发掘图结构数据中的特征和模式，满足聚类、分类、预测、分割、生成等图学习任务需求的算法总称。如何从图数据（网络数据）中学习有效的数据表征是大数据与人工智能时代的一大挑战。为此，本次汇报将从一种特殊的谱图神经网络（图卷积神经网络）出发，介绍相应的消息传递范式，揭示传统机器学习和图神经网络的内在关联，并...
BUILDING CLASSIFICATION OF VHR AIRBORNE STEREO IMAGES USING FULLY CONVOLUTIONAL NETWORKS AND FREE TRAINING SAMPLES
Building Classification VHR Airborne Stereo Images FCN Base Map Mislabels Free Training Samples Fine Tuning Atrous Convolution
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2018/11/9
Semantic segmentation, especially for buildings, from the very high resolution (VHR) airborne images is an important task in urban mapping applications. Nowadays, the deep learning has significantly i...
BUILDING EXTRACTION FROM REMOTE SENSING DATA USING FULLY CONVOLUTIONAL NETWORKS
deep learning DSM fully convolutional networks building footprint binary classification fully connected CRF
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2017/7/13
Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building foot...