4 accepted papers
Recently, contrastive learning has shown promising results for representing graphs. Despite their success, several key issues have not been well addressed in existing studies: 1) Data noise and incompleteness are inevitable in the graph signal due to various factors. 2) Improper augmentation strateg…
As the urbanization accelerates, traffic congestion and accidents problems are more serious. Accurate traffic flow forecasting is essential for urban traffic management and optimization. Recently, graph neural networks (GNNs) based forecasting methods have obtained superior results. However, there a…
Robust and accurate traffic forecasting is a key issue in intelligent transportation systems. Existing studies usually employ pre-defined spatial graph or learned fixed adjacency graph and design models to capture spatial and temporal features. However, pre-defined or fixed graph can not accurately…
Recently, unsupervised learning is proposed to avoid the performance degrading caused by synthesized paired computed tomography (CT) images. However, existing unsupervised methods for metal artifact reduction (MAR) only use features in image space, which is not enough to restore regions heavily corr…