10 accepted papers
Depth completion, the process of generating dense depth maps from sparse or incomplete data, is inherently challenging due to the variability in sensor types, environmental conditions, and the differences between data modalities. In this paper, we propose a novel framework for depth completion. Firs
Point cloud segmentation plays a crucial role in extracting unique attributes and separating various objects, thereby enabling semantic comprehension and analysis. In this paper, we introduce a novel point cloud segmentation approach based on Diffusion Probabilistic Network (DDPM). The proposed mode…
In this study, we apply a graph convolutional network (GCN) in attention deficit hyperactivity disorder (ADHD) classification by using multimodal data. Here, multimodal data is integrated to construct a dual graph for leveraging the modality information. Then, a GCN learning model is performed withi…
We propose a subspace-enhanced hypergraph neural network (seHGNN) for classifying anxiety disorder (AD). By leveraging a learnable incidence matrix, seHGNN strengthens the influence of hyperedges in graphs and enhances feature extraction performance of HGNNs. Then, we conduct this model within an ex…
This paper introduces a novel joint spatio-temporal filtering algorithm and investigate its impact on data alignment in transfer learning (TL) for motion imagery (MI) tasks to deal with the variability in subjects, trials, or tasks. While spatial filtering is an integral part of the common spatial p…
Deep learning methods have been widely applied in Attention Deficit Hyperactivity Disorder (ADHD) classification in the past decade due to their effective learned features. However, these features are lack of neurobiological meanings and hard to be biomarkers. Here, we proposed an attention auto-enc…
Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral disease in school-age children. Its neurobiological classification is meaningful for clinicians. The existing ADHD classification methods suffer from two problems, i.e., insufficient data and noise disturbance. He…
Common spatial pattern (CSP) is widely used in motor imagery classification tasks. Classical CSP depends only on spatial filters. To improve its performance, a novel and efficient spatio-temporal filtering strategy is proposed in this paper to extract discriminative features. Common temporal filters…
Because the spectrum folding phenomenon affects the down-sampling of graph signals, both critically sampled and oversampled graph filter banks with down-sampling operations can only be applied to bipartite graphs. However, general graph signals may not reside on bipartite graph structures. In this p…
In this paper, we propose a novel sparse common spatial pattern (CSP) algorithm to optimally select channels of EEG signals. Compared to the traditional CSP, which maximizes the variance of signals in one class and minimizes the variance of signals in the other class, the classification accuracy is…