GHFormer-Net: Towards more accurate small green apple/begonia fruit detection in the nighttime by Meili Sun, Liancheng Xu, Rong Luo, Yuqi Lu, Weikuan Jia

“…Specifically, PVTv2-B1 based on Transformer is applied as the backbone network to extract feature information from the global receptive, which breaks the limitation that spatial convolution is utilized to extract information from the local area; Next, with the help of FPN, shallow features and high-level features with rich semantic information are incorporated by lateral connections and a top-down structure to generate multi-scale feature maps; Then, a detector of RetinaNet is applied to detect green fruits. …”
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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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Dynamic atrous attention and dual branch context fusion for cross scale Building segmentation in high resolution remote sensing imagery by Yaohui Liu, Shuzhe Zhang, Xinkai Wang, Rui Zhai, Hu Jiang, Lingjia Kong

“…Among them, we introduced the Shift Operation module and the Self-Attention module, which adopt a dual-branch structure to respectively capture local spatial dependencies and global correlations, and perform weight coupling to achieve highly complementary contextual information fusion. …”
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Inpainting of damaged temple murals using edge- and line-guided diffusion patch GAN by G. Sumathi, M. Uma Devi

“…The WSFN uses the original image, a line drawing, and an edge map to capture mural details, which are then texturally inpainted in the SCN using gated convolution for enhanced results. Special attention is given to globally extending the receptive field for large-area inpainting. …”
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Rice Disease Detection: TLI-YOLO Innovative Approach for Enhanced Detection and Mobile Compatibility by Zhuqi Li, Wangyu Wu, Bingcai Wei, Hao Li, Jingbo Zhan, Songtao Deng, Jian Wang

“…As a key global food reserve, rice disease detection technology plays an important role in promoting food production, protecting ecological balance and supporting sustainable agricultural development. …”
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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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Edge-aware joint neural denoising and normal estimation for mobile and handheld laser point clouds by T. Zhang, S. Filin

“…To extract contextual information it introduces densely packed graph convolution layers and a global attention mechanism. …”
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SSATNet: Spectral-spatial attention transformer for hyperspectral corn image classification by Bin Wang, Gongchao Chen, Juan Wen, Linfang Li, Songlin Jin, Yan Li, Ling Zhou, Weidong Zhang

“…Specifically, SSATNet utilizes 3D and 2D convolutions to effectively extract local spatial, spectral, and textural features from the data while incorporating spectral and spatial morphological structures to understand the internal structure of the data better. …”
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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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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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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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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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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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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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Generation driven understanding of localized 3D scenes with 3D diffusion model by Hao Sun, Junping Qin, Zheng Liu, Xinglong Jia, Kai Yan, Lei Wang, Zhiqiang Liu, Shaofei Gong

“…However, the existing diffusion models primarily focus on the global structure and are constrained by predefined dataset categories, which are unable to accurately resolve the detailed structure of complex 3D scenes. …”
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Multiscale Graph Transformer Network With Dynamic Superpixel Pyramid for Hyperspectral Image Classification by Tingting Wang, Yao Sun, Yunfeng Hu

“…To address these limitations, we propose a multi-scale graph transformer network (MSGTN), which captures spatial features at different scales through multiscale graph convolutional networks (GCNs) with adaptive graph structures. …”
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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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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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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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Research on Diffusion Kurtosis Imaging of the Brain Based on Deep Learning by Rui Chen, Jingwen Yue, Rong Li, Zijian Jia

“…The DKI-Transformer model can extract global voxel correlation characteristics, the estimation results have a high structural similarity index compared to the reference labeling and exhibit distinct boundaries of microscopic features. …”
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