A Hybrid CNN Approach with Spatial and Channel Attention ...

This approach involves leveraging hybrid CNNs with spatial and channel attention mechanisms to address challenges like overfitting and model complexity and ...

Hybrid convolution neural network with channel attention ... - Nature

CNN excels in capturing spatial representation from sensor data, while Recurrent Neural Networks (RNN) excel in capturing temporal ...

[2404.14709] SC-HVPPNet: Spatial and Channel Hybrid-Attention ...

Convolutional Neural Network (CNN) and Transformer have attracted much attention recently for video post-processing (VPP). However, the ...

An Efficient Hybrid CNN-Transformer Approach for Remote Sensing ...

Holistic Attention Network (HAN) [41] not only utilizes channel attention and spatial attention to learn the channel and spatial interdependence of features of ...

HAM: Hybrid attention module in deep convolutional neural ...

... channel attention and spatial attention mechanisms simultaneously or bring much additional model complexity. In order to achieve a balance between ...

A hybrid CNN-transformer framework for medical image segmentation

Proposed a TMF block that dynamically fuses semantic information in high-level features. •. Proposed a UA block that combines spatial attention and channel ...

New Fusion Approach of Spatial and Channel Attention for Semantic ...

To address the problem of global semantic context and improve performance in the semantic segmentation of remote sensing images, hybrid CNN has been proposed, ...

Hybrid Spatial-Channel Attention Mechanism for Cross-Age Face ...

Face recognition techniques have been widely employed in real-world biomimetics applications. However, traditional approaches have limitations in ...

Fine-grained image classification method based on hybrid attention ...

DAG-CNN: the method fuses multiple scales of spatial potential representations from different layers of the residual network. The attention ...

Multiscale Hybrid Convolutional Deep Neural Networks with ... - NCBI

... spatial attention mechanisms to the network, such ... channel attention mechanism and presents a pyramid compression hybrid module method.

An Attention-based Hybrid 2D/3D CNN-LSTM for Human Action ...

In most of the literature, 2D CNNs or their 3D counterparts have been used to learn spatial and temporal image-level features of videos. In this paper, we ...

Full article: A multi-scale multi-channel CNN introducing a channel ...

A multi-scale multi-channel CNN introducing a channel-spatial attention mechanism hyperspectral remote sensing image classification method ... proposed a hybrid ...

A Hybrid Network of CNN and Transformer for Lightweight Image ...

Each. HBCT contains a Swin Transformer block (STB) with two. Swin Transformer layers inside, a convolutional layer and two enhanced spatial attention (ESA) ...

Aircraft Image Recognition Network Based on Hybrid Attention ...

In this article, the hybrid attention network model (BA-CNN) to ... The BA-CNN (channel and spatial attention) is used to represent the ...

Channel, spatial, and temporal attention can be regarded as ...

In this paper, we propose a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model for vegetable price forecasting based on ...

Hybrid CNN: An Empirical Analysis of Machine Learning Models for ...

[16] introduced the importance of attention mechanism in Transformer, a novel neural network architecture that relies solely on attention mechanisms,.

Fine-grained image classification method based on hybrid attention ...

DAG-CNN: the method fuses multiple scales of spatial potential representations from different layers of the residual network. The attention ...

Global and pyramid convolutional neural network with hybrid ...

Compared with spectral-based CNN, spatial-based CNN pays more attention to spatial information. To reduce the interference of spectral ...

A Hybrid CNN-LSTM Based Approach for Anomaly Detection ...

... Network (CNN) and Long Short-Term Memory Network (LSTM). The proposed model is capable of capturing the spatial and temporal features of the network traffic.

Attention-based hybrid CNN-LSTM and spectral data augmentation ...

... network model. The approach achieved 89%, 100%, 89% performance in terms of sensitivity, specificity and Kappa measure, respectively.