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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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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Lightweight U-Net for Blood Vessels Segmentation in X-Ray Coronary Angiography by Jesus Salvador Ramos-Cortez, Dora E. Alvarado-Carrillo, Emmanuel Ovalle-Magallanes, Juan Gabriel Avina-Cervantes

“…The pruning method systematically removes entire convolutional filters from each layer based on a global reduction factor, generating compact subnetworks that retain key representational capacity. …”
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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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Graph-Based Adaptive Network With Spatial-Spectral Features for Hyperspectral Unmixing by Hua Dong, Xiaohua Zhang, Jinhua Zhang, Hongyun Meng, Licheng Jiao

“…In the method, HSIs are treated as data on manifold structures, with superpixels serving as graph nodes to construct a global graph-structured data. …”
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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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Deep Learning Model for Precipitation Nowcasting Based on Residual and Attention Mechanisms by Zhan Zhang, Qingping Song, Minzheng Duan, Hailei Liu, Juan Huo, Congzheng Han

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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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MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction by Xu Gao, Xu Gao, Mengfan Yan, Mengfan Yan, Chengwei Zhang, Chengwei Zhang, Gang Wu, Gang Wu, Jiandong Shang, Jiandong Shang, Congxiang Zhang, Congxiang Zhang, Kecheng Yang, Kecheng Yang

“…This model employs Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to extract features from the drug and protein sequences, respectively. …”
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Distributed Photovoltaic Short-Term Power Prediction Based on Personalized Federated Multi-Task Learning by Wenxiang Luo, Yang Shen, Zewen Li, Fangming Deng

“…By improving the parallel pooling structure of a time series convolution network (TCN), an improved time series convolution network (iTCN) prediction model was established, and the channel attention mechanism CBAMANet was added to highlight the key meteorological characteristics’ information and improve the feature extraction ability of time series data in photovoltaic power prediction. …”
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AfaMamba: Adaptive Feature Aggregation With Visual State Space Model for Remote Sensing Images Semantic Segmentation by Hongkun Chen, Huilan Luo, Chanjuan Wang

“…It employs a lightweight ResNet18 as the encoder, and during the decoding phase, it first utilizes a multiscale feature adaptive aggregation module to ensure that the output features from each stage of the encoder contain rich multiscale semantic information. Subsequently, the global-local Mamba structure combines the attention-optimized multiscale convolutional branches with the global branch of Mamba to facilitate effective interaction between global and local features. …”
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Machine Learning Prediction of Storm‐Time High‐Latitude Ionospheric Irregularities From GNSS‐Derived ROTI Maps by Lei Liu, Y. Jade Morton, Yunxiang Liu

“…Abstract This study presents an image‐based convolutional long short‐term memory (convLSTM) machine learning algorithm to predict storm‐time ionospheric irregularities. …”
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Benchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System by Muhenad Bilal, Ranadheer Podishetti, Daniel Grossmann, Markus Bregulla

“…It is compared with conventional diffuse ring illumination to assess its effectiveness in evaluating state-of-the-art convolutional neural networks. This enabled a more targeted investigation of the role of global shape characteristics such as silhouettes versus localized features like the tool face, cutting edges, and delicate geometrical structures under different training strategies. …”
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FD-YOLO: A YOLO Network Optimized for Fall Detection by Hoseong Hwang, Donghyun Kim, Hochul Kim

“…First, a global attention module (GAM) based on the Convolutional Block Attention Module (CBAM) was employed to improve detection performance. …”
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Bitemporal Remote Sensing Change Detection With State-Space Models by Lukun Wang, Qihang Sun, Jiaming Pei, Muhammad Attique Khan, Maryam M. Al Dabel, Yasser D. Al-Otaibi, Ali Kashif Bashir

“…Change detection in very-high-resolution remote sensing images has gained significant attention, particularly with the rise of deep learning techniques such as convolutional neural networks and Transformers. The Mamba structure, successful in computer vision, has been applied to this domain, enhancing computational efficiency. …”
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YOLO-HVS: Infrared Small Target Detection Inspired by the Human Visual System by Xiaoge Wang, Yunlong Sheng, Qun Hao, Haiyuan Hou, Suzhen Nie

“…Based on YOLOv8, we design a multi-scale spatially enhanced attention module (MultiSEAM) using multi-branch depth-separable convolution to suppress background noise and enhance occluded targets, integrating local details and global context. …”
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Infrared object detection for robot vision based on multiple focus diffusion and task interaction alignment by Jixu Zhang, Li Wang, Hung-Wei Li, Meng-Yen Hsieh, Shunxiang Zhang, Hua Wen, Meng Chen

“…The feature extraction module adopts a dual-stream fusion structure in the backbone network, which combines the local feature extraction of CNN with the global feature modeling of transformer. …”
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