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Increase Channel Attention Based on Unet++ Architecture for Medical Images
At present, image segmentation technology has become increasingly mature. The application of image segmentation technology in the field of medical...
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Epilepsy-Net: attention-based 1D-inception network model for epilepsy detection using one-channel and multi-channel EEG signals
In this paper, we propose and evaluate Epilepsy-Net, a collection of deep learning EEG signal processing tools to detect epileptic seizures against...
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Joint channel-spatial attention network for super-resolution image quality assessment
Image super-resolution (SR) is an effective technique to enhance the quality of LR images. However, one of the most fundamental problems for SR is to...
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A prospective approach for human-to-human interaction recognition from Wi-Fi channel data using attention bidirectional gated recurrent neural network with GUI application implementation
Human Activity Recognition (HAR) research has gained significant momentum due to recent technological advancements, artificial intelligence...
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Architectural style classification based on CNN and channel–spatial attention
The accurate classification of architectural styles is of great significance to the study of architectural culture and human historical civilization....
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Convolutional neural network with spatio-temporal-channel attention for remote heart rate estimation
Remote photoplethysmography (rPPG), which measures human heart rate without physical contact with the skin, has become active research in recent...
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Cascade connection-based channel attention network for bidirectional medical image registration
Medical image registration is an essential task in researching and applying medical images. Doctors can observe and extract relevant pathological...
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Multi-channel and multi-scale separable dilated convolutional neural network with attention mechanism for flue-cured tobacco classification
Tobacco classification is a challenging research topic and plays a crucial role in the process of cigarette production. Tobacco classification mainly...
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CACBL-Net: a lightweight skin cancer detection system for portable diagnostic devices using deep learning based channel attention and adaptive class balanced focal loss function
Accurate skin disease detection is one of the most challenging tasks due to high-class imbalance and limited labeled datasets. Recently Deep...
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Residual Feature Distillation Channel Spatial Attention Network for ISP on Smartphone
With the increasing popularity of mobile photography, more and more attention is being paid to image signal processing(ISP) algorithms used to...
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Channel Attention Network for Wireless Capsule Endoscopy Image Super-Resolution
Wireless Capsule Endoscopy (WCE) is a technology used for examination of Gastrointestinal (GI) tract. WCE is comparatively pain-free process to...
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Adaptive Channel Attention-Based Deformable Generative Adversarial Network for Underwater Image Enhancement
In this paper, to effectively strengthen quality of underwater image enhancement from both channel and spatial viewpoints, an adaptive channel...
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Material-aware Cross-channel Interaction Attention (MCIA) for occluded prohibited item detection
For security inspection, detecting prohibited items in X-ray images is challenging since they are usually occluded by non-prohibited items. In X-ray...
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A novel 3D shape recognition method based on double-channel attention residual network
Learning 3D features by deep networks has achieved a successful performance up to now. However, data imbalance and low-resolution voxels still remain...
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Region-based feature enhancement using channel-wise attention for classification of breast histopathological images
Breast histopathological image analysis at 400x magnification is essential for the determination of malignant breast tumours. But manual analysis of...
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LCRCA: image super-resolution using lightweight concatenated residual channel attention networks
Images that are more similar to the original high-resolution images can be generated by deep neural network-based super-resolution methods than the...
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A multi-channel attention graph convolutional neural network for node classification
Graph convolutional neural networks (GCNs) introduced the idea of convolution into graph neural networks. It has been widely used in graph data...
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Group channel pruning and spatial attention distilling for object detection
Due to the over-parameterization of neural networks, many model compression methods based on pruning and quantization have emerged. They are...
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Multiscale Dual-Channel Attention Network for Point Cloud Analysis
Point clouds are the most popular representation of 3D vision tasks and have a wide range of applications in the field of smart robots today. The...
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Channel Spatial Collaborative Attention Network for Fine-Grained Classification of Cervical Cells
Accurately classifying cervical cells based on the commonly used TBS (The Bethesda System) standard is critical for building the automatic cytology...