A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition similar to the convolutional block attention module, an attention layer to each channel and spatial level is applied to improve the model recognition performa... SH Lee,DW Lee,M Kim - ...
To enhance the denoisin gperformance of deep learning by better matching the features of AMT signals, w e propose a convolutionalblock attention module (CBAM)-based method for AMT den oising. This method focuses on the featuresof AMT signals and improves the proc ess from three aspects: 1)...
Few methods have introduced attention mechanisms. For HSI classification tasks, we proposes a model named Hybrid-Convolutional Multi-Attention (HCMA) mechanism. In general, it is a hybrid spectral CNN (HybridSN) combined with Convolutional Block Attention Module (CBAM). The whole model uses 3-D ...
In this paper, we propose a complex convolutional block attention module (CCBAM) to boost the representation power of the complex-valued convolutional layers by constructing more informative features. The CCBAM is a lightweight and general module which can be easily integrated into any complex-...
Recently, it has been demonstrated that the performance of a deep convolutional neural network can be effectively improved by embedding an attention module into it. In this work, a novel lightweight and effective attention method named Pyramid Squeeze Attention (PSA) module is proposed. By replacing...
The multi-scale dilated convolution module aggregates multi-scale context information systematically by making use of dilated convolution without reducing the receiving domain, thereby integrate the underlying detail information into the high-level semantic features to promote the perception and counting ...
Contact classification for human–robot interaction with densely connected convolutional neural network and convolutional block attention module Human–robot interaction (HRI) is a challenging topic to perform various tasks in many repetitive and dangerous tasks. However, humans not only share a wor... ...
Especially, a feature fusion block (FFblock) is introduced as the basic module. Each block can directly read raw global features from previous ones and then learns the feature spatial correlation and channel correlation between features in a holistic way, leading to a continuous global information ...
Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level ...
As such, we propose an adaptive cross-attention-driven spatial-spectral graph convolutional network (ACSS-GCN), which is composed of a spatial GCN (Sa-GCN) subnetwork, a spectral GCN (Se-GCN) subnetwork, and a graph cross-attention fusion module (GCAFM). Specifically, Sa-GCN and Se-GCN ...