A lightweight high-frequency mamba network for image super-resolution by Tao Wu, Wei Xu, Yajuan Wu

“…Various methods based on convolutional neural network (CNN) and Transformer structures have emerged, but few studies have mentioned how to combine these two parts of information. …”
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Fine-Grained Extraction of Coastal Aquaculture Ponds From Remote Sensing Images Using an Edge-Supervised Multi-task Neural Network by Jian Qi, Min Ji, Fengxiang Jin, Jianran Xu, Hanyu Ji, Juan Wang

“…It notably enhances performance in complex environments and significantly boosts generalization capabilities by learning global structural features. First, a shared encoder–decoder architecture was constructed, leveraging large kernel depthwise separable convolution and residual optimization, thereby enhancing both local and global feature representations. …”
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Attention-enhanced StrongSORT for robust vehicle tracking in complex environments by Wei Xu, Xiaodong Du, Ruochen Li, Bingjie Li, Yuhu Jiao, Lei Xing

“…To address these challenges, we propose AE-StrongSORT (Attention-Enhanced StrongSORT), an attention-enhanced tracking framework featuring three systematic innovations: first, the GAM-YOLO (global attention mechanism-YOLO)hybrid architecture integrates multi-scale feature fusion with a global attention mechanism (GC2f structure). …”
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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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Diagnosis of Alzheimer’s disease using brain $$^{18}\textrm{F}$$ -FDG PET imaging based on a state space model by Yufang Dong, Yonglin Chen, Zhe Jin, Xingbo Dong

“…Building on this, we optimized the original purely convolutional structure into a hybrid architecture combining convolution and Transformer layers. …”
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A high-precision edge detection technique for magnetic anomaly signals based on a self-attention mechanism by Ju Haihua, Wang Li, Yang Jie, Liu Gaochuan, Xia Zhong, Jiao Jian, Zhang Le, Dai Bo

“…Magnetic data boundary detection is a key technology in potential field data processing, providing an effective basis for the division of geological units and fault structures. It holds significant importance in geological structure analysis and mineral exploration. …”
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MSDCA: A Multi-Scale Dual-Branch Network with Enhanced Cross-Attention for Hyperspectral Image Classification by Ning Jiang, Shengling Geng, Yuhui Zheng, Le Sun

“…First, a multiscale 3D spatial–spectral feature extraction module (3D-SSF) employs parallel 3D convolutional branches with diverse kernel sizes and dilation rates, enabling hierarchical modeling of spatial–spectral representations from large-scale patches and effectively capturing both fine-grained textures and global context. …”
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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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Power Equipment Image Recognition Method Based on Feature Extraction and Deep Learning by Shuang Lin

“…We plan to introduce a lightweight convolutional structure combined with a graph neural network mechanism to strengthen global context modeling and device structural awareness. …”
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MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph by Yinfei Dai, Yuantong Zhang, Xiuzhen Zhou, Qi Wang, Xiao Song, Shaoqiang Wang

“…In addition, we utilize the graph convolutional neural network (GCN) to process graph-structured data. …”
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NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction by Fatemeh Khoushehgir, Zahra Noshad, Morteza Noshad, Sadegh Sulaimany

“…To optimize prediction accuracy, we employ a hybrid GNN architecture that combines graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE layers, each contributing unique advantages: GraphSAGE offers scalability, GCN provides a global structural perspective, and GAT applies dynamic neighbor weighting. …”
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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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A Spatiotemporal Sequence Prediction Framework Based on Mask Reconstruction: Application to Short-Duration Precipitation Radar Echoes by Zhi Yang, Changzheng Liu, Ping Mei, Lei Wang

“…During pre-training, the model learns global structural features of meteorological systems from sparse contexts by randomly masking local spatiotemporal regions of radar images. …”
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Enhanced Image Retrieval Using Multiscale Deep Feature Fusion in Supervised Hashing by Amina Belalia, Kamel Belloulata, Adil Redaoui

“…By leveraging multiscale features from multiple convolutional layers, MDFF-SH ensures the preservation of fine-grained image details while maintaining global semantic integrity, achieving a harmonious balance that enhances retrieval precision and recall. …”
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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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A security data detection and management method in digital library network based on deep learning by Diyin Zhu, Yihang Wei, Jiali Cai, Jingwen Wang, Zhongshan Chen

“…The method combines the structures of temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU) to extract spatial and temporal features from digital library network security data. …”
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TMAR: 3-D Transformer Network via Masked Autoencoder Regularization for Hyperspectral Sharpening by Zeinab Dehghan, Jingxiang Yang, Mehran Yazdi, Abdolraheem Khader, Liang Xiao

“…In this study, we focus on leveraging the power of CNN and transformer models and propose a multistage deep transformer-based super-resolution network that is regularized via an asymmetric autoencoder structure. In addition, we utilize a 3-D convolution layer in the light transformer structure because it allows for more flexible computation of correlations between HSI layers and better capturing of dependencies within spectral–spatial features. …”
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A Drug-Target Interaction Prediction Method Based on Attention Perception and Modality Fusion by PENG Yang, ZHU Xiaofei, HU Dongdong

“…[Methods] For drug branches, Graph Transformer and Graph Convolutional Neural Network were used to jointly characterize the global structures and biochemical information of drug molecules. …”
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UNestFormer: Enhancing Decoders and Skip Connections With Nested Transformers for Medical Image Segmentation by Adnan Md Tayeb, Tae-Hyong Kim

“…Precise identification of organs and lesions in medical images is essential for accurate disease diagnosis and analysis of organ structures. Deep convolutional neural network (CNN)-based U-shaped networks are among the most popular and promising approaches for this task. …”
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