Astronomical Image Superresolution Reconstruction with Deep Learning for Better Identification of Interacting Galaxies by Jiawei Miao, Liangping Tu, Hao Liu, Jian Zhao

“…To further improve visual quality and enhance the details of galaxy structures, we propose a dual-branch network structure combining convolutional neural networks (CNNs) and Transformer (DBCTNet), which leverages the local characteristics of CNNs to complement the global features of Transformer. …”
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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

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Frequency-Aware Learned Image Compression Using Channel-Wise Attention and Restormer by Hanwen Zhang, Cheolkon Jung, Xu Liu

“…However, learned image compression has a limitation of balancing global context and local texture because the global structure easily ignores local redundancy, especially for the non-repetitive textures, affecting the reconstruction performance. …”
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Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin by Lifu Zheng, Hao Yang, Guichun Luo

“…The UMAP-CNN framework leverages the strengths of manifold learning and deep learning, enabling multi-scale feature extraction and dimensionality reduction while preserving both local and global data structures. The evaluation experiments, which considered runtime, receiver operating characteristic (ROC) curves, embedding distribution maps, and other quantitative assessments, illustrated that the UMAP-CNN outperformed t-distributed stochastic neighbor embedding (t-SNE), locally linear embedding (LLE) and isometric feature mapping (Isomap). …”
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Joint Grid-Based Attention and Multilevel Feature Fusion for Landslide Recognition by Xinran Li, Tao Chen, Gang Liu, Jie Dou, Ruiqing Niu, Antonio Plaza

“…We complement CNNs by adding a transformer-based structure in a layer-by-layer fashion and improving methods for sequence generation and attention weight calculation. …”
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DSMF-Net: Dual Semantic Metric Learning Fusion Network for Few-Shot Aerial Image Semantic Segmentation by Xiyu Qi, Yidan Zhang, Lei Wang, Yifan Wu, Yi Xin, Zhan Chen, Yunping Ge

“…To exploit multiscale global semantic context, we construct scale-aware graph prototypes from different stages of the feature layers based on graph convolutional networks (GCNs), while also incorporating prior-guided metric learning to further enhance context at the high-level convolution features. …”
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Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification by Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar

“…Unlike static fusion methods, our adaptive feature fusion layer employs an attention mechanism to dynamically integrate ResNet50’s localized structural features and vision transformer (ViT) global connectivity patterns, significantly enhancing stage-specific Alzheimer’s disease classification accuracy. …”
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An Adaptive Methodology for Constructing Domain-Specific Sentiment Lexicons Based on Chinese Social Media Data by Xue Xu, Haidong Liu, Lei Liu

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Multi-Scale Venation Pattern Analysis for Medicinal Plant Species Recognition by Arnav Sanjay Karnik, Nikhil Nair, Yashas Sagili, P. B. Pb

“…Our method extracts and analyzes venation patterns at multiple spatial scales, capturing both global and fine-grained structural details to improve classification performance. …”
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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The model comprises four modules: global feature extraction, local feature extraction,causal inference, and dual-channel graph convolution. …”
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A seismic random noise suppression method based on CNN-Mamba by Xiujuan WEI, Xingye LIU, Huailai ZHOU

“…This limitation results in insufficient collaborative optimization between local details and macroscopic structures during denoising, further reducing the noise suppression accuracy. …”
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Multi-Scale Spatial Perception Attention Network for Few-Shot Hyperspectral Image Classification by Yang Li, Jian Luo, Haoyu Long, Qianqian Jin

“…In the encoder, the spatial contraction perception Transformer (SCPFormer) is first proposed to improve the model’s capacity for perceiving global-local joint features. Next, the multi-scale spatial attention (MSSA) module is proposed to capture spatial information at different convolution kernel scales and cascade them to form a more comprehensive representation structure. …”
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Online English teaching resource recommendation method design based on LightGCNCSCM by Jing Tang

“…The research proposes an online English teaching resource recommendation method. The local and global features of the user-resource interaction graph are captured through Lightweight graph convolutional networks, and the resource semantic vectors are extracted in combination with the content-based similarity calculation model. …”
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Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models by Simon Johannes Joham, Arnela Hadzic, Martin Urschler

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Dual-Branch Neural Network-Based In-Loop Filter for VVC Intra Coding Using Spatial-Frequency Feature Fusion by Zhen Feng, Xu Liu, Cheolkon Jung

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Vessel Traffic Flow Prediction in Port Waterways Based on POA-CNN-BiGRU Model by Yumiao Chang, Jianwen Ma, Long Sun, Zeqiu Ma, Yue Zhou

“…Aiming at the stage characteristics of vessel traffic in port waterways in time sequence, which leads to complexity of data in the prediction process and difficulty in adjusting the model parameters, a convolutional neural network (CNN) based on the optimization of the pelican algorithm (POA) and the combination of bi-directional gated recurrent units (BiGRUs) is proposed as a prediction model, and the POA algorithm is used to search for optimized hyper-parameters, and then the iterative optimization of the optimal parameter combinations is input into the best combination of iteratively found parameters, which is input into the CNN-BiGRU model structure for training and prediction. …”
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MultiV_Nm: a prediction method for 2′-O-methylation sites based on multi-view features by Lei Bai, Fei Liu, Yile Wang, Junle Su, Lian Liu

“…MultiV_Nm extracts the features of Nm sites from multiple dimensions, including sequence features, chemical characteristics, and secondary structure features. By integrating the powerful local feature extraction ability of convolutional neural networks, the ability of graph attention networks to capture global structural information, and the efficient interaction advantage of cross-attention mechanisms for different features, it deeply explores and integrates multi-view features, and finally realizes the prediction of Nm modification sites. …”
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Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network by Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

“…Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. …”
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From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification by Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi

“…These conditions are significant global health concerns, affecting millions and leading to vision loss if not diagnosed promptly. …”
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Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments by Qianming SHANG, Wanying JIANG, Yi ZHOU, Zhengqiang WANG, Yubo SUN

“…Meanwhile, a novel parallel dual-channel convolutional neural network structure is designed to explore both global features and deeper, finer details of the data, thereby enhancing the diagnostic performance of the method in strong noise environments.ResultsExperimental evaluation results under different noise conditions show that the proposed method achieves a fault diagnosis accuracy of over 98% in environments with strong noise. …”
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