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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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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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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DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation by Elizar Elizar, Rusdha Muharar, Mohd Asyraf Zulkifley

“…By processing features at different spatial resolutions, the multiscale densely connected layer in the form of the Pyramid Pooling Dense Module (PPDM) helps the network to capture both local and global context, preserving finer details of the cardiac structure while also capturing the broader context required to accurately segment larger cardiac structures. …”
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A Spatial–Frequency Combined Transformer for Cloud Removal of Optical Remote Sensing Images by Fulian Zhao, Chenlong Ding, Xin Li, Runliang Xia, Caifeng Wu, Xin Lyu

“…In order to further enhance the features extracted by DBSA and FreSA, we design the dual-domain feed-forward network (DDFFN), which effectively improves the detail fidelity of the restored image by multi-scale convolution for local refinement and frequency transformation for global structural optimization. …”
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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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Research on SeaTreasure Target Detection Technology Based on Improved YOLOv7-Tiny by Xiang Shi, Yunli Zhao, Jinrong Guo, Yan Liu, Yongqi Zhang

“…First, based on the YOLOv7-Tiny network, the MAFPN neck structure is used to replace the ELAN structure to achieve the multi-scale capture of semantic information of underwater sea treasures, and to enhance the UPA-YOLO model to accurately locate the targets of underwater sea treasures; second, the P2ELAN module is constructed and added to the backbone network, which makes use of the redundancy information in the feature map and dynamically adjusts the convolution kernel to adapt to data The P2ELAN module is added to the backbone network, using the redundant information in the feature map, dynamically adjusting the convolutional kernel to adapt to the lack of data, reducing the number of parameters in the model, and introducing the MSCA attention mechanism to inhibit the complex and changeable background features underwater, to improve the semantic feature extraction ability of the UPA-YOLO model for underwater targets, adding the MPDiou loss function to the improved algorithm model and completing the data validation of the detection model; finally, based on the TensorRT acceleration framework, the optimisation of the target detection Finally, based on the TensorRT acceleration framework, the target detection model is optimised, and the Jetson Nano edge device is used to complete the localisation deployment and realise the real-time target detection task of underwater sea treasures. …”
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A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network by Shuang Liu, Zeng Zhuang, Yanfeng Zheng, Simon Kolmanic

“…Although many neural networks based on U-shaped structures and methods, such as skip connections have achieved excellent results in medical image segmentation tasks, the properties of convolutional operations limit their ability to effectively learn local and global features. …”
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A Lightweight and Rapid Dragon Fruit Detection Method for Harvesting Robots by Fei Yuan, Jinpeng Wang, Wenqin Ding, Song Mei, Chenzhe Fang, Sunan Chen, Hongping Zhou

“…The method builds upon YOLOv10 and integrates Gated Convolution (gConv) into the C2f module, forming a novel C2f-gConv structure that effectively reduces model parameters and computational complexity. …”
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BiEHFFNet: A Water Body Detection Network for SAR Images Based on Bi-Encoder and Hybrid Feature Fusion by Bin Han, Xin Huang, Feng Xue

“…First, a bi-encoder structure based on ResNet and Swin Transformer is used to jointly extract local spatial details and global contextual information, enhancing feature representation in complex scenarios. …”
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Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention by Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang

“…Recently, graph-based methods have also been used to predict trajectories, however processing graph-structured data introduces significant increase in computation. …”
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StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training by Ziqi Yang, Yiran Liao, Ziao Chen, Zhenzhen Lin, Wenyuan Huang, Yanxi Liu, Yuling Liu, Yamin Fan, Jie Xu, Lijia Xu, Jiong Mu

“…Maize (<i>Zea mays</i> L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. …”
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YOLOv8n-WSE-Pest: A Lightweight Deep Learning Model Based on YOLOv8n for Pest Identification in Tea Gardens by Hongxu Li, Wenxia Yuan, Yuxin Xia, Zejun Wang, Junjie He, Qiaomei Wang, Shihao Zhang, Limei Li, Fang Yang, Baijuan Wang

“…The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. …”
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