Spatial and Channel Attention Integration with Separable Squeeze-and-Excitation Networks for Image Classifications by Nazmul Shahadat, Shleshma Regmi, Anup Rijal

“…Our proposed SC-SE layer with 1D CNN block is applied to the SqueezeNext architecture to construct our SC-SE network (SC-SENet). The proposed SC-SENet is designed to effectively capture spatial and channel-wise dependencies within feature maps. …”
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StrawberryNet: Fast and Precise Recognition of Strawberry Disease Based on Channel and Spatial Information Reconstruction by Xiang Li, Lin Jiao, Kang Liu, Qihuang Liu, Ziyan Wang

“…First, to decrease the number of parameters, instead of standard convolution, a partial convolution is selected to construct the backbone for extracting the features of strawberry disease, which can significantly improve efficiency. …”
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SCCA-YOLO: Spatial Channel Fusion and Context-Aware YOLO for Lunar Crater Detection by Jiahao Tang, Boyuan Gu, Tianyou Li, Ying-Bo Lu

“…The Joint Spatial and Channel Fusion Module (SCFM) is utilized to fuse spatial and channel information to model the global relationships between craters and the background, effectively suppressing background noise and reinforcing feature discrimination. …”
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Night construction site detection based on ghost-YOLOX by Han Guijin, Wang Ruixuan, Xu WuYan, Li Jun

“…In order to reduce the number of model parameters and improve the detection speed, the algorithm is based on the Ghost convolution and SimAM self-attention modules build the Sim-Ghost residual module according to the gradient path design strategy, and uses it to reconstruct the backbone and neck networks; In order to improve the detection ability of the network for fuzzy targets and small targets, based on Involution, a Cross Involution Attention (CIA) module is constructed by double cross convolution, and added to the neck network to enable the network to obtain more efficient channel and spatial attention. …”
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Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images by Yang Zhang, Ziwen Wei, Zhihua Liu, Xiaolong Wu, Junchao Qian

“…In the temporal stream, representative frames were extracted from the video to construct stacked grayscale 3-channel images (SG3I) frames. …”
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Rolling Bearing Fault Diagnosis via Temporal-Graph Convolutional Fusion by Fan Li, Yunfeng Li, Dongfeng Wang

“…Simultaneously, frequency-domain features obtained via Fast Fourier Transform (FFT) were used to construct a K-Nearest Neighbors (KNN) graph, which was processed by a Graph Convolutional Network (GCN) to identify spatial correlations. …”
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Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism by Jiade Wu, Yang Ying, Yigao Tan, Zhuliang Liu

“…Focusing on the study of Chinese historical architecture, this research proposes an innovative architectural recognition framework that integrates the Swin Transformer backbone with a custom-designed Global Channel and Spatial Attention (GCSA) mechanism, thereby substantially enhancing the model’s capability to extract architectural details and comprehend global contextual information. …”
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A Novel Spectral-Spatial Attention Network for Zero-Shot Pansharpening by Hailiang Lu, Mercedes E. Paoletti, Juan M. Haut, Sergio Moreno-Alvarez, Guangsheng Chen, Weipeng Jing

“…As a result, by integrating 3-D convolutional neural networks (3DCNN), spatial attention and channel attention, <monospace>ZSPNet</monospace> is capable of accurately reconstructing MS with enhanced spatial resolution. …”
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RSM-YOLOv11: Lightweight Steel Surface Defect Segmentation Algorithm Research Based on YOLOv11 Improvement by Zenghai Shan, Hu Haoyan, Changjian Zhu, Shaowen Du, Hongtao Jing, Wang Haibin

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Multi-Scale Differentiated Network with Spatial–Spectral Co-Operative Attention for Hyperspectral Image Denoising by Xueli Chang, Xiaodong Wang, Xiaoyu Huang, Meng Yan, Luxiao Cheng

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Crop classification with deep convolutional neural network based on crop feature by Mohamad Reza Gili, Davoud Ashourloo, Hosein Aghighi, Ali Akbar Matkan, Alireza SHakiba

“…The purpose of this study is to use a deep learning method based on convolutional networks to classify the crop types and improve the performance of this network by using feature channels as an input image to the network and increasing the classification accuracy. …”
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3D long time spatiotemporal convolution for complex transfer sequence prediction by Qiu Yunan, Cui Yingjie, Tang Haibo, Chen Zhongfeng, Lu Zhenyu, Xue Feng

“…Secondly, a cross-structured spatio-temporal attention module is constructed based on spatio-temporal features in the decoding stage to enhance the response of fine features in the image in the convolutional channel, so as to capture non-smooth local features. …”
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Human Action Recognition Method Based on Multi-channel Fusion by Zhiyong TAO, Xijun GUO, Xiaokui REN, Ying LIU, Zemin WANG

“…Initially, a multi-channel information extraction model is constructed to leverage the spatial diversity inherent in MIMO systems. …”
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Real-Time Transformer Detection of Underwater Objects Based on Lightweight Gated Convolutional Network by Yuhui LI, Huixia CUI, Yaomin LI, Senping JIA

“…To address the challenges in underwater object detection algorithms, including difficult image feature processing, redundant model architectures, and excessive parameter numbers, this paper proposed a real-time Transformer detection method for underwater objects based on a lightweight gated convolutional network. This method first constructed a convolutional gated linear unit based on the gating mechanism to dynamically modulate feature transmission. …”
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Lightweight Multiscale Spatio-Temporal Graph Convolutional Network for Skeleton-Based Action Recognition by Zhiyun Zheng, Qilong Yuan, Huaizhu Zhang, Yizhou Wang, Junfeng Wang

“…To solve these problems, the Lightweight Multiscale Spatio-Temporal Graph Convolutional Network (LMSTGCN) is proposed. Firstly, the Lightweight Multiscale Spatial Graph Convolutional Network (LMSGCN) is constructed to capture the information in various hierarchies, and multiple inner connections between skeleton joints are captured by dividing the input features into a number of subsets along the channel direction. …”
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FCN attention enhancing asphalt pavement crack detection through attention mechanisms and fully convolutional networks by Huiyuan Zhang, Jiawei Liu, Guoping Hu

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Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition by Hao Chao, Liang Dong, Yongli Liu, Baoyun Lu

“…Finally, a deep learning model designed with two principal component analysis convolutional layers and a nonlinear transformation operation extracted the spatial characteristics and global interchannel synchronization features from the constructed feature images, which were then input to support vector machines to perform the emotion recognition tasks. …”
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EOST-LSTM: Long Short-Term Memory Model Combined with Attention Module and Full-Dimensional Dynamic Convolution Module by Guangxin He, Wei Wu, Jing Han, Jingjia Luo, Lei Lei

“…The full-dimensional dynamic convolutional module introduces the dynamic attention mechanism in the spatial position and input and output channels of the convolutional kernel, adaptively adjusts the weight of the convolutional kernel, and improves the flexibility and efficiency of feature extraction. …”
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Less Is More: Brain Functional Connectivity Empowered Generalizable Intention Classification With Task-Relevant Channel Selection by Haowei Lou, Zesheng Ye, Lina Yao, Yu Zhang

“…Meanwhile, despite previous studies using either convolutional neural networks (CNNs) or graph neural networks (GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. …”
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Multi-Channel Speech Enhancement Using Labelled Random Finite Sets and a Neural Beamformer in Cocktail Party Scenario by Jayanta Datta, Ali Dehghan Firoozabadi, David Zabala-Blanco, Francisco R. Castillo-Soria

“…In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. …”
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