25 citations · 52 across the 8 of their papers we have counts for
8 papers
EarthLD: Towards Unified Open-World Landslide Understanding via Vision-Language Guided Diffusion Models
Yuanchao Su, Lianru Gao, Mengying Jiang +3
Landslides are widespread geological hazards, yet their automated detection and mapping in remote sensing imagery remain challenging because of their irregular morphology, ambiguou…
Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
Mengying Jiang, Guizhong Liu, Yuanchao Su +1
In representation learning on graph-structured data, many popular graph neural networks (GNNs) fail to capture long-range dependencies, leading to performance degradation. Furtherm…
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions
Mengying Jiang, Guizhong Liu, Yuanchao Su +2
Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit c…
Relation-aware graph structure embedding with co-contrastive learning for drug-drug interaction prediction
Mengying Jiang, Guizhong Liu, Biao Zhao +2
Relation-aware graph structure embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-…
GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily
Mengying Jiang, Guizhong Liu, Yuanchao Su +1
In representation learning on the graph-structured data, under heterophily (or low homophily), many popular GNNs may fail to capture long-range dependencies, which leads to their p…
R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph
Xinliang Wu, Mengying Jiang, Guizhong Liu
Heterogeneous graph is a kind of data structure widely existing in real life. Nowadays, the research of graph neural network on heterogeneous graph has become more and more popular…
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image
Faxian Cao, Zhijing Yang, Jinchang Ren +2
As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral im…
Does Normalization Methods Play a Role for Hyperspectral Image Classification?
Faxian Cao, Zhijing Yang, Jinchang Ren +2
For Hyperspectral image (HSI) datasets, each class have their salient feature and classifiers classify HSI datasets according to the class's saliency features, however, there will…