Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…However, most existing graph structures primarily focus on local similarity while overlooking global semantic correlation. …”
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DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion by Yong Ji, Wenbin Shi, Jingsheng Lei, Jiayin Ding

“…In DBRSNet, the Feature-Guided Selection Module (FGSM) adaptively integrates complementary features from CNN and Transformer branches, while the Convolutional Attention Integration Module (CAIM) enhances global dependencies and spectral correlations, ensuring a more comprehensive feature representation. …”
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A High-Precision Defect Detection Approach Based on BiFDRep-YOLOv8n for Small Target Defects in Photovoltaic Modules by Yi Lu, Chunsong Du, Xu Li, Shaowei Liang, Qian Zhang, Zhenghui Zhao

“…With the accelerated transition of the global energy structure towards decarbonization, the share of PV power generation in the power system continues to rise. …”
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Edge-aware joint neural denoising and normal estimation for mobile and handheld laser point clouds by T. Zhang, S. Filin

“…To extract contextual information it introduces densely packed graph convolution layers and a global attention mechanism. …”
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Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images by Feifeng Liu, Weihu Wang

“…The structure of a convolution kernel learned by using parallel network structure achieves better classification performance. …”
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DEFIF-Net: A lightweight dual-encoding feature interaction fusion network for medical image segmentation. by Zhanlin Ji, Shengnan Hao, Quanming Zhao, Zidong Yu, Hongjiu Liu, Lei Li, Ivan Ganchev

“…Firstly, in the encoding stage of DEFIF-Net, a global dependency fusion branch is introduced as an additional encoder to capture distant feature dependencies, whereby the neighboring and distant feature dependencies are effectively integrated by the newly designed feature interaction fusion convolution. …”
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A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment by Jiwen Jia, Junhua Kang, Lin Chen, Xiang Gao, Borui Zhang, Guijun Yang

“…The evaluated models include both self-supervised and supervised approaches, employing different network structures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). …”
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Multilevel Feature Gated Fusion Based Spatial and Frequency Domain Attention Network for Joint Classification of Hyperspectral and LiDAR Data by Cuiping Shi, Zhipeng Zhong, Shihang Ding, Yeqi Lei, Liguo Wang, Zhan Jin

“…Hyperspectral images provide rich spectral information, while LiDAR data supplements three-dimensional spatial structural information. The combination of the two can effectively improve the accuracy of land cover classification. …”
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Research on Vehicle Target Detection Method Based on Improved YOLOv8 by Mengchen Zhang, Zhenyou Zhang

“…A Lightweight Shared Convolution Detection Head was designed. By designing a shared convolution layer through group normalization, the detection head of the original model was improved, which can reduce redundant calculations and parameters and enhance the ability of global information fusion between feature maps, thereby achieving the purpose of improving computational efficiency. …”
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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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Fiber Visualization with LIC Maps Using Multidirectional Anisotropic Glyph Samples by Mark Höller, Kay-M. Otto, Uwe Klose, Samuel Groeschel, Hans-H. Ehricke

“…Line integral convolution (LIC) is used as a texture-based technique in computer graphics for flow field visualization. …”
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DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments by Jun Zhang, Dongfang Zhang, Jingyan Liu, Yuhong Zhou, Xiaoshuo Cui, Xiaofei Fan

“…Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. …”
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Downhole Coal–Rock Recognition Based on Joint Migration and Enhanced Multidimensional Full-Scale Visual Features by Bin Jiao, Chuanmeng Sun, Sichao Qin, Wenbo Wang, Yu Wang, Zhibo Wu, Yong Li, Dawei Shen

“…Additionally, a multi-scale luminance adjustment module is integrated to merge features across perceptual ranges, mitigating localized brightness anomalies such as overexposure. The model is structured around an encoder–decoder backbone, enhanced by a full-scale connectivity mechanism, a residual attention block with dilated convolution, Res2Block elements, and a composite loss function. …”
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Enhancing Crop Health: Advanced Machine Learning Techniques for Prediction Disease in Palm Oil Tree by Nandy Manish, Kumar Yalakala Dinesh

“…This study builds predictive models by using a palmd database comprised of the large datasets of palm oil tree health indicators, environmental factors and historical disease outbreaks to identify early signs of disease with high accuracy.To analyze both structured as well as unstructured data multiple machine learning algorithms were used such as Random Forest, Support Vector Machines, Convolution Neural Networks. …”
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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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Deep learning-based sex estimation of 3D hyoid bone models in a Croatian population using adapted PointNet++ network by Ivan Jerković, Željana Bašić, Ivana Kružić

“…The model, optimized for small datasets with 1D convolutional layers and global size features, was first applied in an unsupervised framework. …”
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An Mcformer encoder integrating Mamba and Cgmlp for improved acoustic feature extraction by Nurmemet Yolwas, Yongchao Li, Lixu Sun, Jian Peng, Zhiwu Sun, Yajie Wei, Yineng Cai

“…To address this limitation, the Mcformer encoder is introduced, which incorporates the Mamba module in parallel with multi-head attention blocks to enhance the model’s global context processing capabilities. Additionally, a Convolutional Gated Multilayer Perceptron (Cgmlp) structure is employed to improve the extraction of local features through deep convolutional layers. …”
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Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism. by Tingyuan Zhang, Changsheng Zhang, Zhongyi Yang, Meng Wang, Fujie Zhang, Dekai Li, Sen Yang

“…The RSCD-Net architecture consists of 16 layers of SCR-Blocks, structured into four convolutional stages with 3, 4, 6, and 3 units, respectively. …”
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Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models. by Zenglei Liu, Ailian Gao, Hui Sheng, Xueling Wang

“…The majority of deep learning techniques developed for medical image analysis rely on convolutional modules to extract the inherent structure of images within a certain local receptive field. …”
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Bearing fault diagnosis based on efficient cross space multiscale CNN transformer parallelism by Qi Chen, Feng Zhang, Yin Wang, Qing Yu, Genfeng Lang, Lixiong Zeng

“…Subsequently, parallel branches are employed to extract spatio-temporal features: the Convolutional Neural Network (CNN) branch integrates a multiscale feature extraction module, a Reversed Residual Structure (RRS), and an Efficient Multiscale Attention (EMA) mechanism to enhance local and global feature extraction capabilities; the Transformer branch combines Bidirectional Gated Recurrent Units (BiGRU) and Transformer to capture both local temporal dynamics and long-term dependencies. …”
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