Structure-aware positional transformer for visible-infrared person re-identification
Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval problem,
which aims at matching the same pedestrian between the visible and infrared cameras. Due …
which aims at matching the same pedestrian between the visible and infrared cameras. Due …
Reverse graph learning for graph neural network
Graph neural networks (GNNs) conduct feature learning by taking into account the local
structure preservation of the data to produce discriminative features, but need to address the …
structure preservation of the data to produce discriminative features, but need to address the …
Multigraph fusion for dynamic graph convolutional network
Graph convolutional network (GCN) outputs powerful representation by considering the
structure information of the data to conduct representation learning, but its robustness is …
structure information of the data to conduct representation learning, but its robustness is …
Adaptive reverse graph learning for robust subspace learning
Subspace learning decreases the dimensions for high-dimensional data by projecting the
original data into a low-dimensional subspace, as well as preserving the similarity among …
original data into a low-dimensional subspace, as well as preserving the similarity among …
Aspect sentiment analysis with heterogeneous graph neural networks
G Lu, J Li, J Wei - Information Processing & Management, 2022 - Elsevier
Aspect-based sentiment analysis technologies may be a very practical methodology for
securities trading, commodity sales, movie rating websites, etc. Most recent studies adopt …
securities trading, commodity sales, movie rating websites, etc. Most recent studies adopt …
Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data
In the early diagnosis of the Coronavirus disease (COVID-19), it is of great importance for
either distinguishing severe cases from mild cases or predicting the conversion time that …
either distinguishing severe cases from mild cases or predicting the conversion time that …
MTGCN: A multi-task approach for node classification and link prediction in graph data
Z Wu, M Zhan, H Zhang, Q Luo, K Tang - Information Processing & …, 2022 - Elsevier
Both node classification and link prediction are popular topics of supervised learning on the
graph data, but previous works seldom integrate them together to capture their …
graph data, but previous works seldom integrate them together to capture their …
Graph convolutional network with sample and feature weights for Alzheimer's disease diagnosis
Either traditional learning methods or deep learning methods have been widely applied for
the early Alzheimer's disease (AD) diagnosis, but these methods often suffer from the issue …
the early Alzheimer's disease (AD) diagnosis, but these methods often suffer from the issue …
Robust graph learning with graph convolutional network
Y Wan, C Yuan, M Zhan, L Chen - Information Processing & Management, 2022 - Elsevier
Graph convolutional network (GCN) is a powerful tool to process the graph data and has
achieved satisfactory performance in the task of node classification. In general, GCN uses a …
achieved satisfactory performance in the task of node classification. In general, GCN uses a …
Fsnet: dual interpretable graph convolutional network for alzheimer's disease analysis
Graph Convolutional Networks (GCNs) are widely used in medical images diagnostic
research, because they can automatically learn powerful and robust feature representations …
research, because they can automatically learn powerful and robust feature representations …