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HOGFormer: high-order graph convolution transformer for 3D human pose estimation
The combination of graph convolution network (GCN) and Transformer has shown promising results in 3D human pose estimation (HPE) tasks when lifting...
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Comparative analysis of open-source federated learning frameworks - a literature-based survey and review
While Federated Learning (FL) provides a privacy-preserving approach to analyze sensitive data without centralizing training data, the field lacks an...
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Supernode Fusion Model Based on Bimodal Action Recognition
Skeleton action recognition based on graph convolutional neural networks (GCNs) has become a hot research topic in recent years. Existing graph...
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Joint recognition for location and activity based on multidimensional features of CSI images
The joint recognition technology of location and activity, leveraging CSI (Channel State Information), finds widespread applications in domains such...
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Determining the Number of Clusters in Clinical Response of TMS Treatment using Hyperdimensional Computing
This paper addresses clustering of clinical response of subjects with major depressive disorder (MDD) after they are treated with transcranial...
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Enhanced spatial–temporal dynamics in pose forecasting through multi-graph convolution networks
Recently, there has been a growing interest in predicting human motion, which involves forecasting future body poses based on observed pose...
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A Knowledge-enhanced model with syntactic-aware attentive graph convolutional network for biomedical entity and relation extraction
Biomedical entity and relation extraction has recently gained increasing interest. Nevertheless, it continues to pose a significant challenge due to...
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Research on load excitation identification method of multi-connected air conditioning compressor based on RBF network with multi-strategy fusion SSA
The load excitation of the multi-connected air conditioning compressor is the major source of vibration in the entire air conditioning pipeline...
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WTGCN: wavelet transform graph convolution network for pedestrian trajectory prediction
The task of pedestrian trajectory prediction remains challenging due to variable scenarios, complex social interactions, and uncertainty in...
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GFD-SSL: generative federated knowledge distillation-based semi-supervised learning
Federated semi-supervised learning (Fed-SSL) algorithms have been developed to address the challenges of decentralized data access, data...
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Unsupervised image categorization based on deep generative models with disentangled representations and von Mises-Fisher distributions
Variational autoencoders (VAEs) have emerged as powerful deep generative models for learning abstract representations in the latent space, making...
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DmADs-Net: dense multiscale attention and depth-supervised network for medical image segmentation
Deep learning has made important contributions to the development of medical image segmentation. Convolutional neural networks, as a crucial branch,...
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EEMNet: an end-to-end efficient model for PCB surface tiny defect detection
The miniaturization of electronic products has led to the denser and more crowded wiring on printed circuit boards (PCBs), which has made PCB defects...
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Subspace learning via Hessian regularized latent representation learning with \({l}_{2,0}\)-norm constraint: unsupervised feature selection
Unsupervised feature selection techniques have shown promising results in dealing with unlabelled high-dimensional data. Laplacian graph-based...
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Axiomatic approaches to three types of L-valued rough sets
In this paper, considering L being a GL-quantale, we further develop the theory of L -valued rough sets with an L -set as the basic universe of...
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Exploiting graph neural network with one-shot learning for fault diagnosis of rotating machinery
Insufficient training data often leads to overfitting, posing a significant challenge in diagnosing faults in mechanical devices, particularly...
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Channel spatio-temporal convolutional network for pedestrian trajectory prediction
Pedestrian trajectory prediction is a crucial technology for agents to assist human beings, which remains highly challenging due to the complex...
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Deep question generation model based on dual attention guidance
Question generation refers to the automatic generation of questions by computer systems based on given paragraphs and answers, which is one of the...
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Contrastive visual feature filtering for generalized zero-shot learning
Generalized zero-shot learning aims to classify images from seen and unseen classes only by training with seen samples, which encounters the...
