Improved Face Image Super-Resolution Model Based on Generative Adversarial Network by Qingyu Liu, Yeguo Sun, Lei Chen, Lei Liu

“…Furthermore, a multi-scale discriminator with a weighted sub-discriminator loss is developed to balance global structural and local detail generation quality. …”
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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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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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Multiscale Graph Transformer Network With Dynamic Superpixel Pyramid for Hyperspectral Image Classification by Tingting Wang, Yao Sun, Yunfeng Hu

“…To address these limitations, we propose a multi-scale graph transformer network (MSGTN), which captures spatial features at different scales through multiscale graph convolutional networks (GCNs) with adaptive graph structures. …”
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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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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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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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Detection Method for Bolts with Mission Pins on Transmission Lines Based on DBSCAN-FPN by Zhenbing ZHAO, Shuai ZHANG, Wei JIANG, Peng WU

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Improved stereo matching network based on dense multi-scale feature guided cost aggregation by ZHANG Bo, ZHANG Meiling, LI Xue, ZHU Lei

“…Firstly, a dense multi-scale feature extraction module was designed based on the dense atrous spatial pyramid pooling structure. This module extracted region-level features of different scales by using atrous convolution of different expansion rates, and effectively fused image features of different scales through dense connection, so that the network can capture contextual information. …”
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Research on Diffusion Kurtosis Imaging of the Brain Based on Deep Learning by Rui Chen, Jingwen Yue, Rong Li, Zijian Jia

“…The DKI-Transformer model can extract global voxel correlation characteristics, the estimation results have a high structural similarity index compared to the reference labeling and exhibit distinct boundaries of microscopic features. …”
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Improving Cerebrovascular Imaging with Deep Learning: Semantic Segmentation for Time-of-Flight Magnetic Resonance Angiography Maximum Intensity Projection Image Enhancement by Tomonari Yamada, Takaaki Yoshimura, Shota Ichikawa, Hiroyuki Sugimori

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An industrial carbon block instance segmentation algorithm based on improved YOLOv8 by Runjie Shi, Zhengbao Li, Zewei Wu, Wenxin Zhang, Yihang Xu, Gan Luo, Pingchuan Ma, Zheng Zhang

“…YOLOv8-HDSA adds a convolutional self-attention mechanism with residual structure to the head, preserving important local information of carbon blocks and improving the ability to extract fine-grained edge details and global features of carbon blocks. …”
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BDSER-InceptionNet: A Novel Method for Near-Infrared Spectroscopy Model Transfer Based on Deep Learning and Balanced Distribution Adaptation by Jianghai Chen, Jie Ling, Nana Lei, Lingqiao Li

“…The key contributions include: (1) RX-Inception multi-scale structure: Combines Xception’s depthwise separable convolution with ResNet’s residual connections to strengthen global–local feature coupling. (2) Squeeze-and-Excitation (SE) attention: Dynamically recalibrates spectral band weights to enhance discriminative feature representation. (3) Systematic evaluation of six transfer strategies: Comparative analysis of their impacts on model adaptation performance. …”
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Cross-Domain Person Re-Identification Based on Multi-Branch Pose-Guided Occlusion Generation by Pengnan Liu, Yanchen Wang, Yunlong Li, Deqiang Cheng, Feixiang Xu

“…Secondly, a multi-branch feature fusion structure is constructed. By fusing different feature information from the global and occlusion branches, the diversity of features is enriched. …”
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