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Image Super-Resolution Based on Gated Residual and Gated Convolution Networks
Single image super-resolution based on deep neural network has been a hot topic in recent years. In this paper, we propose a gated residual and gated...
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IPCRGC-YOLOv7: face mask detection algorithm based on improved partial convolution and recursive gated convolution
In complex scenarios, current detection algorithms often face challenges such as misdetection and omission when identifying irregularities in...
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Bridging partial-gated convolution with transformer for smooth-variation image inpainting
Deep learning has brought essential improvement to image inpainting technology. Conventional deep-learning methods primarily focus on creating...
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Spatial-temporal graph neural network based on gated convolution and topological attention for traffic flow prediction
Accurate traffic flow prediction is essential for develo** intelligent transportation systems (ITS) and providing real-time traffic applications....
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A gated graph attention network based on dual graph convolution for node embedding
The research on node classification is based on node embeddings. Node classification accuracy can be improved if the embeddings of different nodes...
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R-GCN: a residual-gated recurrent unit convolution network model for anomaly detection in blockchain transactions
The domain of deep learning has provided an exemplary paradigm for how Artificial Intelligence (AI) can be a disruptive technological paragon through...
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Ga-RFR: Recurrent Feature Reasoning with Gated Convolution for Chinese Inscriptions Image Inpainting
Inscriptions were a primary means of recording historical events and literary works in ancient times, and remain an important part of Chinese ancient...
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AGG: attention-based gated convolutional GAN with prior guidance for image inpainting
Image inpainting has made great achievements recently, but it is often tough to generate a semantically consistent image when faced with large...
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Towards Diverse Binary Segmentation via a Simple yet General Gated Network
In many binary segmentation tasks, most CNNs-based methods use a U-shape encoder-decoder network as their basic structure. They ignore two key...
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Attribute prediction of spatio-temporal graph nodes based on weighted graph diffusion convolution network
Spatio-temporal graph data can be analyzed by effectively mining for realizing spatio-temporal graph data prediction. It is of great significance to...
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Dual temporal gated multi-graph convolution network for taxi demand prediction
Taxi demand prediction is essential to build efficient traffic transportation systems for smart city. It helps to properly allocate vehicles, ease...
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STGHTN: Spatial-temporal gated hybrid transformer network for traffic flow forecasting
Accurate traffic forecasting is a critical function of intelligent transportation systems, which remains challenging due to the complex spatial and...
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Gated Fusion Adaptive Graph Neural Network for Urban Road Traffic Flow Prediction
Accurate prediction of traffic flow plays an important role in maintaining traffic order and traffic safety, which is a key task in the application...
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A period-extracted multi-featured dynamic graph convolution network for traffic demand prediction
Urban online car-hailing demand prediction poses a significant challenge in develo** intelligent transportation systems due to its intricate and...
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A unified framework for backpropagation-free soft and hard gated graph neural networks
We propose a framework for the definition of neural models for graphs that do not rely on backpropagation for training, thus making learning more...
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Mini-3DCvT: a lightweight lip-reading method based on 3D convolution visual transformer
Lip-reading has attracted more and more attention in recent years, and has wide application prospects and value in areas such as human–computer...
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Recurrent convolutional model based on gated spiking neural P system for stereo matching networks
The rapid development of deep learning techniques has introduced extensive research improvements to various aspects in the processing pipeline of the...
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Dynamic convolution-based image dehazing network
Convolutional neural networks use a convolutional kernel with static weights for processing non-uniform haze or dense fog, which may lead to...
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Convolutional Gated MLP: Combining Convolutions and gMLP
To the best of our knowledge, this is the first paper to introduce Convolutions to Gated Multi-Layer Perceptron (gMLP) and contributes an...
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Scaled gated networks
Gating transformation demonstrates great potential in recent deep convolutional neural networks design, enriching the feature representation and...
