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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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. …”
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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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VM-UNet++ research on crack image segmentation based on improved VM-UNet by Wenliang Tang, Ziyi Wu, Wei Wang, Youqin Pan, Weihua Gan

“…Abstract Cracks are common defects in physical structures, and if not detected and addressed in a timely manner, they can pose a severe threat to the overall safety of the structure. …”
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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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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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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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Antenna Optimization Design Based on Deep Gaussian Process Model by Xin-Yu Zhang, Yu-Bo Tian, Xie Zheng

“…In order to solve this problem, this study constructs a deep GP (DGP) model by using the structural form of convolutional neural network (CNN) and combining it with GP. …”
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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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TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions by Huizhi Xu, Wenting Tan, Yamei Li, Yue Tian

“…The model employs multi-scale convolutional operations combined with an Efficient Channel Attention (ECA) module to extract discriminative local features, while independent convolutional layers capture hierarchical global representations. …”
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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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A Hybrid Learnable Fusion of ConvNeXt and Swin Transformer for Optimized Image Classification by Jaber Qezelbash-Chamak, Karen Hicklin

“…However, each paradigm alone is limited in addressing both fine-grained structures and broader anatomical context. We propose ConvTransGFusion, a hybrid model that fuses ConvNeXt (for refined convolutional features) and Swin Transformer (for hierarchical global attention) using a learnable dual-attention gating mechanism. …”
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ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting by Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao

“…In the proposed model, structure-based and location-based localized spatial features are obtained simultaneously by Graph Convolutional Networks (GCNs) and DeepWalk. …”
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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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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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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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Improved YOLOv8s-based foreign object detection method for mine conveyor belts by LI Runze, GUO Xingge, YANG Fazhan, ZHAO Peipei, XIE Guolong

“…The core feature extraction and fusion module C2f was improved by VMamba's Visual State Space (VSS) module, which efficiently captured global contextual information in images through a state space model and four-directional scanning mechanism, enhancing the model’s understanding of global image structure. …”
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InGSA: integrating generalized self-attention in CNN for Alzheimer's disease classification by Faisal Binzagr, Anas W. Abulfaraj

“…Furthermore, several GSA heads are used to exploit other dependency structures of global features as well. Our evaluation of InGSA on a two benchmark dataset, using various pre-trained networks, demonstrates the GSA's superior performance.…”
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FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction by Xuekai Zhu, Juan Liu, Jian Zhang, Zhihui Yang, Feng Yang, Xiaolei Zhang

“…Owing to the structural limitations of CNN, features extracted from this method are local patterns that lack global information. …”
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