YOLO-DAFS: A Composite-Enhanced Underwater Object Detection Algorithm by Shengfu Luo, Chao Dong, Guixin Dong, Rongmin Chen, Bing Zheng, Ming Xiang, Peng Zhang, Zhanwei Li

“…The backbone incorporates a DualBottleneck module to enhance feature extraction, replacing the standard bottleneck structure in C3k, thus enhancing the feature extraction and the channel aggregation. …”
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Adaptive Pixel-Level and Superpixel-Level Feature Fusion Transformer for Hyperspectral Image Classification by Wei Huang, Dazhan Zhou, Le Sun, Qiqiang Chen, Junru Yin

“…However, graph convolutional networks (GCNs) can effectively extract features from the global structure. …”
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Reliable Multistate RRAM Devices for Reconfigurable CAM and IMC Applications by Shengpeng Xing, Zijian Wang, Zhen Wang, Pengtao Li, Xuemeng Fan, Ziyang Zhang, Guobin Zhang, Jianhao Kan, Qi Luo, Shuai Zhong, Yishu Zhang

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Enhancing Cross-Domain Remote Sensing Scene Classification by Multi-Source Subdomain Distribution Alignment Network by Yong Wang, Zhehao Shu, Yinzhi Feng, Rui Liu, Qiusheng Cao, Danping Li, Lei Wang

“…To alleviate these issues, we present a Multi-Source Subdomain Distribution Alignment Network (MSSDANet), which introduces novel network structures and loss functions for subdomain-oriented MSDA. …”
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CrysMTM: a multiphase, temperature-resolved, multimodal dataset for crystalline materials by Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban

“…This multimodal structure enables both supervised and self-supervised learning across graph-based, image-based, and language-based architectures. …”
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Multimodal lightweight neural network for Alzheimer's disease diagnosis integrating neuroimaging and cognitive scores by Bhoomi Gupta, Ganesh Kanna Jegannathan, Mohammad Shabbir Alam, Kottala Sri Yogi, Janjhyam Venkata Naga Ramesh, Vemula Jasmine Sowmya, Isa Bayhan

“…In the neuroimaging feature extraction module, redundancy-reduced convolutional operations are employed to capture fine-grained local features, while a global filtering mechanism enables the extraction of holistic spatial patterns. …”
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CNN–Transformer Hybrid Architecture for Underwater Sonar Image Segmentation by Juan Lei, Huigang Wang, Zelin Lei, Jiayuan Li, Shaowei Rong

“…FLSSNet is built upon a CNN and Transformer backbone network, integrating four core submodules to address various technical challenges: (1) The asymmetric dual encoder–decoder (ADED) is capable of simultaneously extracting features from different modalities and systematically modeling both local contextual information and global spatial structure. (2) The Transformer feature converter (TFC) module optimizes the multimodal feature fusion process through feature transformation and channel compression. (3) The long-range correlation attention (LRCA) module enhances CNN’s ability to model long-range dependencies through the collaborative use of convolutional kernels, selective sequential scanning, and attention mechanisms, while effectively suppressing noise interference. (4) The recursive contour refinement (RCR) model refines edge contour information through a layer-by-layer recursive mechanism, achieving greater precision in boundary details. …”
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An Efficient Semantic Segmentation Framework with Attention-Driven Context Enhancement and Dynamic Fusion for Autonomous Driving by Jia Tian, Peizeng Xin, Xinlu Bai, Zhiguo Xiao, Nianfeng Li

“…Recognizing the limitations of convolutional networks in modeling long-range dependencies and capturing global semantic context, the model incorporates an attention-based feature extraction component. …”
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Thyroid nodule segmentation in ultrasound images using transformer models with masked autoencoder pre-training by Yi Xiang, Rajendra Acharya, Quan Le, Jen Hong Tan, Chiaw-Ling Chng

“…Unlike traditional convolutional neural networks (CNNs), transformers capture global context from the first layer, enabling more comprehensive image representation, which is crucial for identifying subtle nodule boundaries. …”
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ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting by Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao

“…In the proposed model, structure-based and location-based localized spatial features are obtained simultaneously by Graph Convolutional Networks (GCNs) and DeepWalk. …”
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A lightweight steel surface defect detection network based on YOLOv9 by Tianyi Zheng, Ling Yu, Yongbao Shi, Fanglin Niu

“…Next, we replace the regular convolution blocks in the model network with spatial-to-depth convolutions, further reducing the model’s computational complexity while retaining global feature information. …”
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Intelligent recognition method for personnel intrusion hazardous area in fully mechanized mining face by Qinghua MAO, Jiao ZHAI, Xin HU, Yinan SU, Xusheng XUE

“…The adaptive fusion ability of the model for multi-scale personnel features is enhanced through the improved SPC-ASFF (Adaptive Structure Feature Fusion with Sub-Pixel Convolution layer). …”
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A Spatiotemporal-Adaptive-Network-Based Method for Predicting Axial Forces in Assembly Steel Struts with Servo System of Foundation Pits by Weiwei Liu, Jianchao Sheng, Jian Zhou, Jinbo Fu, Wangjing Yao, Kuan Chang, Zhe Wang

“…Due to its high sensitivity to temperature variations and direct influence on the lateral deformation of the foundation pit enclosure structure, accurate prediction is essential for safety monitoring and early warning. …”
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Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization by Shiguo Huang, Linyu Cao, Ruili Sun, Tiefeng Ma, Shuangzhe Liu

“…Moreover, we incorporate the self-attention mechanism into the GCN to extract deeper data features and employ k-reciprocal NN to enhance the accuracy and robustness of the graph structure in the GCN. In the second stage, we employ the Global Minimum Variance (GMV) model for portfolio optimization, culminating in the AGC-CNN+GMV two-stage approach. …”
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SSATNet: Spectral-spatial attention transformer for hyperspectral corn image classification by Bin Wang, Gongchao Chen, Juan Wen, Linfang Li, Songlin Jin, Yan Li, Ling Zhou, Weidong Zhang

“…Specifically, SSATNet utilizes 3D and 2D convolutions to effectively extract local spatial, spectral, and textural features from the data while incorporating spectral and spatial morphological structures to understand the internal structure of the data better. …”
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Short-term rainfall prediction based on radar echo using an efficient spatio-temporal recurrent unit by Dali Wu, Shunli Zhang, Guohong Zhao, Yongchao Feng, Yuan Ma, Yue Zhang

“…The combined effect of the Self-Attention (SA) mechanism and convolution allows the model to focus on both global and local dependencies in spatial information, improving the clarity of the generated images. …”
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Adaptive Spectral Correlation Learning Neural Network for Hyperspectral Image Classification by Wei-Ye Wang, Yang-Jun Deng, Yuan-Ping Xu, Ben-Jun Guo, Chao-Long Zhang, Heng-Chao Li

“…Although some existing deep neural networks have exploited the rich spectral information contained in HSIs for land cover classification by designing some adaptive learning modules, these modules were usually designed as additional submodules rather than basic structural units for building backbones, and they failed to adaptively model the spectral correlations between adjacent spectral bands and nonadjacent bands from a local and global perspective. …”
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Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Defects such as cracks, delamination, erosion, and icing not only compromise the structural integrity of blades but also significantly reduce their aerodynamic efficiency and energy production capabilities. …”
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Attention-Enhanced Hybrid Automatic Modulation Classification for Advanced Wireless Communication Systems: A Deep Learning-Transformer Framework by Sam Ansari, Khawla A. Alnajjar, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny, Abir Hussain, Soliman Mahmoud

“…To address these limitations, this paper presents a novel attention-enhanced hybrid AMC framework that synergistically integrates specialized convolutional layers for efficient temporal feature extraction with a compact transformer encoder for global sequence modeling. …”
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MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models by Feng Wang, Jinming Chu, Liyan Shen, Shan Chang

“…Finally, MESM uses Graph Convolutional Network (GCN) and SubgraphGCN to extract global and local features from the perspective of the overall graph and subgraphs. …”
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