Výsledky vyhledávání - "graph encoder and decoder"

CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification Autor Liu, Qichao, Xiao, Liang, Yang, Jingxiang, Wei, Zhihui

“…Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional…”
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Contextual-Aware Land Cover Classification With U-Shaped Object Graph Neural Network Autor Zhao, Wenzhi, Peng, Shu, Chen, Jiage, Peng, Rui

“…), which is mainly composed of self-adaptive graph construction (SAGC), hierarchical graph encoder, and decoder…”
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Hyperspectral Multilevel GCN and CNN Feature Fusion for Change Detection Autor Katiyar, Chhaya, Manian, Vidya

“…Hyperspectral image (HSI) change detection focuses on identifying differences in multitemporal HSIs. Graph convolutional networks (GCNs) have demonstrated…”
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Data Driven Event Analysis for Cyber-Power System Autor Ahmed, Arman

“…Power grid is prone to a diverse set of events, namely, power, cyber, and cyber-induced power events. Time-synchronized data from Phasor Measurement Units…”
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Hybrid Approach of Convolutional and Graph Convolutional Networks with Pixel-and Superpixel-Level Feature Fusion and Attention Mechanism for SAR Image Classification Autor Peter, Eno, Ang, Li Minn, Srivastava, Sanjeev, Seng, Kah Phooi

“… The spatial attention mechanism helps the model focus on relevant parts of the SAR image. The model uses graph encoder and decoder architectures to exchange features between image pixels and graph nodes, capturing both local and global features…”
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Deep Learning for Tackling Spatiotemporal Forecasting Problems Autor Feng, Bo

“…Spatiotemporal forecasting problems cover a wide range of machine learning tasks in which spatial and temporal features are tightly coupled. For example,…”
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G2GT: Retrosynthesis Prediction with Graph-to-Graph Attention Neural Network and Self-Training Autor Lin, Zaiyun, Yin, Shiqiu, Shi, Lei, Zhou, Wenbiao, Zhang, Yingsheng John

“… To address this challenge, we propose a novel graph-to-graph transformation model, G2GT. The model is built on the standard transformer structure and utilizes graph encoders and decoders…”
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