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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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Boosting person ReID feature extraction via dynamic convolution
Extraction of discriminative features is crucial in person re-identification (ReID) which aims to match a query image of a person to her/his images,...
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Dense-scale dynamic network with filter-varying atrous convolution for semantic segmentation
Deep convolution neural networks (DCNNs) in deep learning have been widely used in semantic segmentation. However, the filters of most regular...
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Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification
Pneumonia is a serious disease that can be fatal, particularly among children and the elderly. The accuracy of pneumonia diagnosis can be improved by...
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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 coupled generative graph convolution network by capturing dynamic relationship of regional flow for traffic prediction
Traffic flow prediction plays a critical role in urban traffic management and planning. Accurate prediction of traffic flow can enhance traffic...
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Lightweight network for small target fall detection based on feature fusion and dynamic convolution
The accurate and prompt detection of falls in the elderly holds significant importance in building a fall detection system based on artificial...
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Efficient ship detection in sar images with dynamic feature smoothing and visual module using omni-dimensional dynamic large-scale convolution
In SAR (Synthetic Aperture Radar) image ship detection tasks, the accuracy of detection is hindered by the presence of a significant amount of...
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Single Image Dehazing Based on Dynamic Convolution and Transformer
In this paper, an end-to-end multi-stage dehazing network based on convolution and Transformer is proposed. The network design is divided into three...
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CoCluster-DAGCN: a dynamic aggregate graph convolution network by a co-attention LSTM cluster for ocean temperature predictions
Considering that ocean temperature data contain much irrelevant noise if a traditional method is used to obtain a temperature prediction, it will be...
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Dynamic Circular Convolution for Image Classification
In recent years, Vision Transformer (ViT) has achieved an outstanding landmark in disentangling diverse information of visual inputs, superseding...
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Object Tracking with Channel Group Regularization and Smooth Constraints Using Improved Dynamic Convolution Kernels in ITS
Aiming at the problem that the correlation between multi-channel feature representation and filter structure is not considered in the objective...
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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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AttCluster-MDGCNs: multiscale dynamic graph convolution networks with an attention cluster for skeletal-based action
Traditional graph convolution networks have been widely applied to recognize skeleton action tasks and have achieved great success. However, it is...
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KernelFlexSR: a self-adaptive super-resolution algorithm with multi-path convolution and residual network for dynamic kernel enhancement
Machine learning-based image super-resolution (SR) has garnered increasing research interest in recent years. However, there are two issues that have...
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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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GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction
Dynamic network link prediction is becoming a hot topic in network science, due to its wide applications in biology, sociology, economy and industry....
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EEG-Based Subject-Independent Depression Detection Using Dynamic Convolution and Feature Adaptation
Depression is a debilitating condition that can seriously impact quality of life, and existing clinical diagnoses are often complicated and dependent...
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An improved dynamic Chebyshev graph convolution network for traffic flow prediction with spatial-temporal attention
Accurate traffic flow prediction plays a significant role in urban traffic management, including traffic congestion control and public travel route...
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DTCC: Multi-level dilated convolution with transformer for weakly-supervised crowd counting
Crowd counting provides an important foundation for public security and urban management. Due to the existence of small targets and large density...
