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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Joint Spectral Information and Spatial Details for Road Extraction From Optical Remote-Sensing Images by Yuzhun Lin, Jie Rui, Fei Jin, Shuxiang Wang, Xibing Zuo, Xiao Liu

“…Currently, satellite remote-sensing image acquisition systems typically include two forms of panchromatic and multispectral images, both of which have complementary advantages in spatial and channel dimensions. However, translating advantageous information into a deciphering function in road-extraction tasks remains a challenge. …”
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Convolutional Neural Networks—Long Short-Term Memory—Attention: A Novel Model for Wear State Prediction Based on Oil Monitoring Data by Ying Du, Hui Wei, Tao Shao, Shishuai Chen, Jianlei Wang, Chunguo Zhou, Yanchao Zhang

“…To address this, a CNN–LSTM–Attention network is specially constructed for predicting wear state, which hierarchically integrates convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal dynamics modeling, and self-attention mechanisms for adaptive feature refinement. …”
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SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase by Yu Zhao, Fei Liu, Qiang He, Fang Liu, Xiaohu Sun, Jiyong Zhang

“…With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. …”
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FUSE-Net: Multi-Scale CNN for NIR Band Prediction from RGB Using GNDVI-Guided Green Channel Enhancement by Gwanghyeong Lee, Deepak Ghimire, Donghoon Kim, Sewoon Cho, Byoungjun Kim, Sunghwan Jeong

“…Building on this, we introduce FUSE-Net, a novel deep learning model that combines multi-scale convolutional layers and MLP-Mixer-based channel learning to effectively model spatial and spectral dependencies. …”
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Foot Pressure-Based Abnormal Gait Recognition With Multi-Scale Cross-Attention Fusion by Menghao Yuan, Yan Wang, Xiaohu Zhou, Meijiang Gui, Aihui Wang, Chen Wang, Guotao Li, Hongnian Yu, Lin Meng, Zengguang Hou

“…MSCAF-Gait incorporates multi-scale convolutional modules with channel and spatial attention mechanisms to effectively capture features across temporal, channel, and spatial dimensions. …”
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SG-ResNet: Spatially Adaptive Gabor Residual Networks with Density-Peak Guidance for Joint Image Steganalysis and Payload Location by Zhengliang Lai, Chenyi Wu, Xishun Zhu, Jianhua Wu, Guiqin Duan

“…The reconstruction subnet synchronously constructs multi-scale features, preserves steganographic spatial fingerprints with channel-separated residual spatial rich model and pixel reorganization operators, and achieves sub-pixel-level steganographic localization via iterative optimization mechanism of feedback residual modules. …”
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MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…Furthermore, the encoder attention fusion module (EAF) is employed, where spatial and channel attention weights are generated using dual-path pooling to achieve the adaptive fusion of deep and shallow layer features. …”
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Behavior Analysis of Students in Preschool Mathematics Teaching Based on Deep Learning by Guangning Qin

“…To solve this problem, this paper proposes a novel students classroom behaviors based on YOLOv8 deep learning model. Combining the channel attention mechanism with deep convolution, a dynamic channel attention convolution (DCAConv) is proposed, which can dynamically adjust the channel weights and capture key features more sensitively. …”
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Single Infrared Super-Resolution via a Shifted Full-Scale Non-Local Network by Honghong Lu, Zhenhua Li

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Osteosarcoma CT Image Segmentation Based on OSCA-TransUnet Model by Zhuoming Fan, Qian Wang, Jun Wang, Yu Liu, Yong Zeng, Jimin Chen

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Fracture identification and 3D reconstruction of coal-rock combinations based on VRA-UNet network by Dengke WANG, Longhang WANG, Yaguang QIN, Le WEI, Tanggen CAO, Wenrui LI, Lu LI, Xu CHEN, Yuling XIA

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Trajectory Prediction and Intention Recognition Based on CNN-GRU by Jinghao Du, Dongdong Lu, Fei Li, Ke Liu, Xiaolan Qiu

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NGSTGAN: N-Gram Swin Transformer and Multi-Attention U-Net Discriminator for Efficient Multi-Spectral Remote Sensing Image Super-Resolution by Chao Zhan, Chunyang Wang, Bibo Lu, Wei Yang, Xian Zhang, Gaige Wang

“…The discriminator enhances attention to multi-scale key features through the addition of channel, spatial, and pixel attention (CSPA) modules, while the generator utilizes an improved shallow feature extraction (ISFE) module to extract multi-scale and multi-directional features, enhancing the capture of complex textures and details. …”
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HCAFNet: Hierarchical Cross-Modal Attention Fusion Network for HSI and LiDAR Joint Classification by Jiajia Bai, Na Chen, Jiangtao Peng, Lanxin Wu, Weiwei Sun, Zhijing Ye

“…However, these methods often use the channel or spatial dimension attentions to highlight features, which overlook the interdependencies between these dimensions and face challenges in effectively extracting and fusing diverse features from heterogeneous datasets. …”
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Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n by XU Jinhui, WANG Wenshan, WANG Shuang, WANG Wenyue, ZHAO Tingting

“…Based on YOLOv8n, the Convolutional Block Attention Module (CBAM) was introduced, enhancing the extraction of key features through spatial and channel attention mechanisms. …”
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MB-MSTFNet: A Multi-Band Spatio-Temporal Attention Network for EEG Sensor-Based Emotion Recognition by Cheng Fang, Sitong Liu, Bing Gao

“…The model constructs a 3D tensor to encode band–space–time correlations of sensor data, explicitly modeling frequency-domain dynamics and spatial distributions of EEG sensors across brain regions. …”
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YOLOv10n-Based Defect Detection in Power Insulators: Attention Enhancement and Feature Fusion Optimization by Zhihao Wei, Yan Wei

“…The channel-space dual attention mechanism (CBAM) is integrated into the C2f module, and the channel weights are computed by global average pooling and maximum pooling in parallel, combined with the spatial attention features extracted by convolution, to realize the accurate focusing on defect-related channels and spatial regions, and the experimental results show that the improved model’s mAP@50 reaches 94.2%, which is 2.4% better than that of the baseline model YOLOv10n, and better than that of YOLOv5n. …”
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Detection of water surface targets based on improved Deformable DETR by Pengjiu WANG, Junbin Gong, Wei LUO, Xiao HUANG, Junjie GUO

“…The spatial attention module first applies pooling operations along the channel dimension of the feature map refined by the channel attention module, followed by convolution and Sigmoid activation to obtain spatial attention features. …”
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