SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection by Zhihui Yu, Nian Pan, Jin Zhou

“…Then, we design the visual-Mamba-based global information extension (VMamba-GIE) module, which leverages a multi-branch structure combining the capability of convolutional layers to extract features in local space with the advantages of state space models in the exploration of long-distance information. …”
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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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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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GAT-Enhanced YOLOv8_L with Dilated Encoder for Multi-Scale Space Object Detection by Haifeng Zhang, Han Ai, Donglin Xue, Zeyu He, Haoran Zhu, Delian Liu, Jianzhong Cao, Chao Mei

“…The local features extracted by convolutional neural networks are mapped to graph-structured data, and the nodal attention mechanism of GAT is used to capture the global topological association of space objects, which makes up for the deficiency of the convolutional operation in weight allocation and realizes GAT integration. …”
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Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents by Yipeng Zhou, Jiaxin Fan, Qingchuan Zhang, Lin Zhu, Xingchen Sun

“…By constructing a document–sentence–entity heterogeneous graph and employing graph convolutional networks (GCNs), the model effectively captures global semantic associations and interactions between cross-sentence triggers and arguments. …”
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Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning by Teemu Koivisto, Marcin Mińkowski, Lasse Laurson

“…We first train a convolutional neural network using data from molecular dynamics simulations to learn the mapping from the sample-specific initial atomic structure to features of the stress–strain curve. …”
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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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Rain removal method for single image of dual-branch joint network based on sparse transformer by Fangfang Qin, Zongpu Jia, Xiaoyan Pang, Shan Zhao

“…Indeed, RSTB preserves the most valuable self-attention values for the aggregation of features, facilitating high-quality image reconstruction from a global perspective. Finally, the parallel dual-branch joint module, composed of RSTB and UEDB branches, effectively captures the local context and global structure, culminating in a clear background image. …”
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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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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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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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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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MFMamba: A Mamba-Based Multi-Modal Fusion Network for Semantic Segmentation of Remote Sensing Images by Yan Wang, Li Cao, He Deng

“…Specifically, the network employs a dual-branch encoding structure, consisting of a CNN-based main encoder for extracting local features from high-resolution remote sensing images (HRRSIs) and of a Mamba-based auxiliary encoder for capturing global features on its corresponding digital surface model (DSM). …”
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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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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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A deep fusion‐based vision transformer for breast cancer classification by Ahsan Fiaz, Basit Raza, Muhammad Faheem, Aadil Raza

“…Cancerous tissue detection in histopathological images relies on complex features related to tissue structure and staining properties. Convolutional neural network (CNN) models like ResNet50, Inception‐V1, and VGG‐16, while useful in many applications, cannot capture the patterns of cell layers and staining properties. …”
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Real-time diagnosis of multi-category skin diseases based on IR-VGG by Ling TAN, Shanshan RONG, Jingming XIA, Sarker SAJIB, Wenjie MA

“…Malignant skin lesions have a very high cure rate in the early stage.In recent years, dermatological diagnosis research based on deep learning has been continuously promoted, with high diagnostic accuracy.However, computational resource consumption is huge and it relies on large computing equipment in hospitals.In order to realize rapid and accurate diagnosis of skin diseases on Internet of things (IoT) mobile devices, a real-time diagnosis system of multiple categories of skin diseases based on inverted residual visual geometry group (IR-VGG) was proposed.The contour detection algorithm was used to segment the lesion area of skin image.The convolutional block of the first layer of VGG16 was replaced with reverse residual block to reduce the network parameter weight and memory overhead.The original image and the segmented lesion image was inputed into IR-VGG network, and the dermatological diagnosis results after global and local feature extraction were outputed.The experimental results show that the IR-VGG network structure can achieve 94.71% and 85.28% accuracy in Skindata-1 and Skindata-2 skin diseases data sets respectively, and can effectively reduce complexity, making it easier for the diagnostic system to make real-time skin diseases diagnosis on IoT mobile devices.…”
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