Attention-enhanced StrongSORT for robust vehicle tracking in complex environments by Wei Xu, Xiaodong Du, Ruochen Li, Bingjie Li, Yuhu Jiao, Lei Xing

“…To address these challenges, we propose AE-StrongSORT (Attention-Enhanced StrongSORT), an attention-enhanced tracking framework featuring three systematic innovations: first, the GAM-YOLO (global attention mechanism-YOLO)hybrid architecture integrates multi-scale feature fusion with a global attention mechanism (GC2f structure). …”
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Improvement in Pavement Defect Scenarios Using an Improved YOLOv10 with ECA Attention, RefConv and WIoU by Xiaolin Zhang, Lei Lu, Hanyun Luo, Lei Wang

“…The RefConv dual-branch structure achieves feature complementarity between local details and global context (mAP increased by 2.1%), the ECA mechanism models channel relationships using 1D convolution (small-object recall rate increased by 27%), and the WIoU loss optimizes difficult sample regression through a dynamic weighting mechanism (location accuracy improved by 37%). …”
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Foreign object detection on coal conveyor belt enhanced by attention mechanism by ZHANG Yang, CHENG Zhiyu, CHEN Yunjiang, ZHANG Jiannan, YUAN Wensheng, ZHANG Hui

“…A unique combination of convolution and pooling operations was used by the CPCA attention mechanism to perform global average pooling and maximum pooling on the input feature map, multi-dimensional feature information was deeply mined, and then attention weights for each channel and spatial position were accurately generated through nonlinear transformation, guiding the model to focus on the key feature areas of foreign objects and enhance feature extraction capabilities. …”
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MSDCA: A Multi-Scale Dual-Branch Network with Enhanced Cross-Attention for Hyperspectral Image Classification by Ning Jiang, Shengling Geng, Yuhui Zheng, Le Sun

“…First, a multiscale 3D spatial–spectral feature extraction module (3D-SSF) employs parallel 3D convolutional branches with diverse kernel sizes and dilation rates, enabling hierarchical modeling of spatial–spectral representations from large-scale patches and effectively capturing both fine-grained textures and global context. …”
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A Malware Classification Method Based on Knowledge Distillation and Feature Fusion by Xin Guan, Guodong Zhang

“…This approach incorporates image texture features with enhanced Local Binary Pattern (LBP), providing insights into the local structure and layout of images and aiding the model in better understanding image details and internal structure, thus enhancing classification performance. …”
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A small object detection model in aerial images based on CPDD-YOLOv8 by Jingyang Wang, Jiayao Gao, Bo Zhang

“…Firstly, we propose the C2fGAM structure, which integrates the Global Attention Mechanism (GAM) into the C2f structure of the backbone so that the model can better understand the overall semantics of the images. …”
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Feature Interaction and Adaptive Fusion Network With Spectral Modulation for Pansharpening by Lihua Jian, Jiabo Liu, Lihui Chen, Di Zhang, Gemine Vivone, Xichuan Zhou

“…In addition, a residual structure-based self-guided spatial-channel adaptive convolution is introduced to accommodate diverse features within FASA adaptively. …”
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Surface Defect and Malformation Characteristics Detection for Fresh Sweet Cherries Based on YOLOv8-DCPF Method by Yilin Liu, Xiang Han, Longlong Ren, Wei Ma, Baoyou Liu, Changrong Sheng, Yuepeng Song, Qingda Li

“…First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. …”
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Enhanced Cross-stage-attention U-Net for esophageal target volume segmentation by Xiao Lou, Juan Zhu, Jian Yang, Youzhe Zhu, Huazhong Shu, Baosheng Li

“…WRA was employed to capture global attention, whose large convolution kernel was further decomposed to simplify the calculation. …”
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A lightweight high-frequency mamba network for image super-resolution by Tao Wu, Wei Xu, Yajuan Wu

“…Various methods based on convolutional neural network (CNN) and Transformer structures have emerged, but few studies have mentioned how to combine these two parts of information. …”
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NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction by Fatemeh Khoushehgir, Zahra Noshad, Morteza Noshad, Sadegh Sulaimany

“…To optimize prediction accuracy, we employ a hybrid GNN architecture that combines graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE layers, each contributing unique advantages: GraphSAGE offers scalability, GCN provides a global structural perspective, and GAT applies dynamic neighbor weighting. …”
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Diagnosis of Alzheimer’s disease using brain $$^{18}\textrm{F}$$ -FDG PET imaging based on a state space model by Yufang Dong, Yonglin Chen, Zhe Jin, Xingbo Dong

“…Building on this, we optimized the original purely convolutional structure into a hybrid architecture combining convolution and Transformer layers. …”
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DFAST: A Differential-Frequency Attention-Based Band Selection Transformer for Hyperspectral Image Classification by Deren Fu, Yiliang Zeng, Jiahong Zhao

“…A 3D convolution and a spectral–spatial attention mechanism are applied to perform fine-grained modeling of spectral and spatial features, further enhancing the global dependency capture of spectral–spatial features. …”
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YOLO-SWD—An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios by Ruyan Zhou, Mingkang Gu, Haiyan Pan

“…Three improved modules are introduced: the DLKA module enhances the perception of local details and global context through dynamic deformable convolution and large receptive field attention mechanisms; the CKSP module improves the model’s ability to extract target boundaries and shapes; and the WTHead enhances the diversity and robustness of feature extraction. …”
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A Spatiotemporal Sequence Prediction Framework Based on Mask Reconstruction: Application to Short-Duration Precipitation Radar Echoes by Zhi Yang, Changzheng Liu, Ping Mei, Lei Wang

“…During pre-training, the model learns global structural features of meteorological systems from sparse contexts by randomly masking local spatiotemporal regions of radar images. …”
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Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence by Na Song, Jing Yang, Xuemei Fu, Xiangli Yang, Ying Xie, Shiping Wang

“…Simultaneously, a learnable graph neural network captures global topology using a graph structure-level reconstruction loss. …”
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MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph by Yinfei Dai, Yuantong Zhang, Xiuzhen Zhou, Qi Wang, Xiao Song, Shaoqiang Wang

“…In addition, we utilize the graph convolutional neural network (GCN) to process graph-structured data. …”
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DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation by Haiyan Liu, Yu Zeng, Hao Li, Fuxin Wang, Jianjun Chang, Huaping Guo, Jian Zhang

“…Additionally, the authors incorporate a self‐attention mechanism to capture global semantic information of high‐level features to guide the extraction and processing of low‐level features, thereby enhancing the model's understanding of the overall structure while maintaining details. …”
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Robust SAR-assisted cloud removal via supervised align-guided fusion and bidirectional hybrid reconstruction by Anling Wang, Kai Xu, Wenxin Wang, Taoyang Wang, Zhaohong Jia, Chengcheng Fan

“…The bidirectional hybrid reconstruction module integrates global and local information via the parallel combination of convolution and transformer layers to ensure consistent filling in both the central and boundary areas. …”
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YOLOv10-kiwi: a YOLOv10-based lightweight kiwifruit detection model in trellised orchards by Jie Ren, Wendong Wang, Yuan Tian, Jinrong He

“…Second, to further reduce model complexity, a novel C2fDualHet module is proposed by integrating two consecutive Heterogeneous Kernel Convolution (HetConv) layers as a replacement for the traditional Bottleneck structure. …”
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