MCPA: multi-scale cross perceptron attention network for 2D medical image segmentation by Liang Xu, Mingxiao Chen, Yi Cheng, Pengwu Song, Pengfei Shao, Shuwei Shen, Peng Yao, Ronald X. Xu

“…However, it faces challenges in capturing long-range dependencies due to the limited receptive fields and inherent bias of convolutional operations. Recently, numerous transformer-based techniques have been incorporated into the UNet architecture to overcome this limitation by effectively capturing global feature correlations. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…First, investigated the effects of different global information extraction methods on the experimental results; second, analyzed the effects of different modules on the network effects; third, explored the impact of different scales on network performance, sequential cascade structure, and rationalization of hierarchical feature fusion; and fourth, verified the robustness of the enhancement modules designed by testing them on different backbone networks. …”
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PI-ADFM: Enhancing Multimodal Remote Sensing Image Matching Through Phase-Integrated Aggregated Deep Features by Haiqing He, Shixun Yu, Yongjun Zhang, Yufeng Zhu, Ting Chen, Fuyang Zhou

“…Geometric distortions and significant nonlinear radiometric differences in multimodal remote sensing images (MRSIs) introduce substantial noise in feature extraction. Single-branch convolutional neural networks fail to capture global image features and integrate local and global information effectively, yielding deep descriptors with low discriminability and limited robustness. …”
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Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach by Justice Williams Asare, Emmanuel Akwah Kyei, Seth Alornyo, Emmanuel Freeman, Martin Mabeifam Ujakpa, William Leslie Brown‐Acquaye, Alfred Coleman, Forgor Lempogo

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High-Precision Qiantang River Water Body Recognition Based on Remote Sensing Image by Hongcui Wang, Yihong Zheng, Ouxiang Chen

“…., are applied, Currently there are few works on the water body identification of Qiantang River, Here, one major challenge for high-precision Qiantang water body recognition is the real complex water body features and complicated geological environment, They are the dense distribution of small water bodies in the Qiantang River Basin, large differences in water body nutrition, and the high complexity of surface environments such as mountains and plains, We investigated two traditional and several deep learning methods and found that WatNet was the most effective model for Qiantang River, This model adopts the structure based on encoder-decoder convolutional network, It uses MobileNetV2 as the encoder, which makes it extract more water feature information while being lightweight and uses ASPP module to capture global multi-scale features in deep layers, Experimental results show that the MIoU and OA (Overall Accuracy) can reach 0. 97 and 0. 99 respectively.…”
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U-Shaped Dual Attention Vision Mamba Network for Satellite Remote Sensing Single-Image Dehazing by Tangyu Sui, Guangfeng Xiang, Feinan Chen, Yang Li, Xiayu Tao, Jiazu Zhou, Jin Hong, Zhenwei Qiu

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Global Optical and SAR Image Registration Method Based on Local Distortion Division by Bangjie Li, Dongdong Guan, Yuzhen Xie, Xiaolong Zheng, Zhengsheng Chen, Lefei Pan, Weiheng Zhao, Deliang Xiang

“…We further design a Multi-Feature Fusion Capsule Network (MFFCN) that integrates shallow salient features with deep structural details, reconstructing the dimensions of digital capsules to generate feature descriptors encompassing texture, phase, structure, and amplitude information. …”
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Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN by Haijun Liu, Yan Ma, Huijun Le, Liangchao Li, Rui Zhou, Jian Xiao, Weifeng Shan, Zhongxiu Wu, Yalan Li

“…The highlights of our work include the following: (1) for the first time, a dual memory mechanism was utilized in TEC prediction, which can more fully capture the temporal and spatial features; (2) we modified the n vs. n structure of original PredRNN to an encoder–decoder structure, so as to handle the problem of unequal input and output lengths in TEC prediction; and (3) we expanded the feature channels by extending the Kp, Dst, and F10.7 to the same spatiotemporal resolution as global TEC maps, overlaying them together to form multichannel features, so as to fully utilize the influence of solar and geomagnetic activities on TEC. …”
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GU-Net3+: A Global-Local Feature Fusion Algorithm for Building Extraction in Remote Sensing Images by Yali Liu, Cui Ni, Peng Wang, Dongqing Yang, Hexin Yuan, Chao Ma

“…In remote sensing image building extraction, image regions with similar textures or colors often cause false positives and false negatives in building-detection. Global features can help the model better recognize the overall structure of large buildings and provide contextual background information when segmenting small buildings to avoid mis-segmentation. …”
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HGLFNet: Hybrid Global Semantic and Local Detail Feature Network for Lane Detection by Lei Ding, Chunhui Tang, Yi Fang

“…HGLFNet effectively integrates global semantic context with local detailed information, enhancing the network’s ability to detect thin and occluded lane line structures. …”
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Quantifying the non-isomorphism of global urban road networks using GNNs and graph kernels by Linfang Tian, Weixiong Rao, Kai Zhao, Huy T. Vo

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ViT-DualAtt: An efficient pornographic image classification method based on Vision Transformer with dual attention by Zengyu Cai, Liusen Xu, Jianwei Zhang, Yuan Feng, Liang Zhu, Fangmei Liu

“…The model adopts a CNN-Transformer hierarchical structure, combining the strengths of Convolutional Neural Networks (CNNs) and Transformers to effectively capture and integrate both local and global features, thereby enhancing feature representation accuracy and diversity. …”
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Two-stream spatio-temporal GCN-transformer networks for skeleton-based action recognition by Dong Chen, Mingdong Chen, Peisong Wu, Mengtao Wu, Tao Zhang, Chuanqi Li

“…This study proposes a novel architecture addressing this limitation by implementing a parallel configuration of GCNs and the Transformer model (SA-TDGFormer). This parallel structure integrates the advantages of both the GCN model and the Transformer model, facilitating the extraction of both local and global spatio-temporal features, leading to more accurate motion information encoding and improved recognition performance. …”
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DGCFNet: Dual Global Context Fusion Network for remote sensing image semantic segmentation by Yuan Liao, Tongchi Zhou, Lu Li, Jinming Li, Jiuhao Shen, Askar Hamdulla

“…While Transformer can extract long-range contextual information through multi-head self attention mechanism, which has significant advantages in capturing global feature dependencies. To achieve high-precision semantic segmentation of remote sensing images, this article proposes a novel remote sensing image semantic segmentation network, named the Dual Global Context Fusion Network (DGCFNet), which is based on an encoder-decoder structure and integrates the advantages of CNN in capturing local information and Transformer in establishing remote contextual information. …”
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Optical flow estimation based on global cross information and dynamic encoder–dynamic decoder by Haoxin Guo, Yifan Wang, Xiaobo Guo

“…To solve the problem that the lack of a global perspective leads to local misestimation and overall structural dislocation when optical flow estimates large-scale motion and complex scenes, this paper proposes an optical flow estimation based on global cross information and dynamic encoder–dynamic decoder. …”
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Multi-scale conv-attention U-Net for medical image segmentation by Peng Pan, Chengxue Zhang, Jingbo Sun, Lina Guo

“…Abstract U-Net-based network structures are widely used in medical image segmentation. …”
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Action recognition using part and attention enhanced feature fusion by Danfeng Zhuang, Min Jiang, Lei Wang, Mohamed Sanim Akremi, Hedi Tabia

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On the modifications of one method for rendering global illumination using a generative adversarial neural network by Rubinov Kirill, Frolov Alexander

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CNN–Transformer gated fusion network for medical image super-resolution by Juanjuan Qin, Jian Xiong, Zhantu Liang

“…The network consists of two branches, one is the global branch based on residual Transformer network, and the other is the local branch based on dynamic convolutional neural network. …”
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Improving unsupervised pedestrian re‐identification with enhanced feature representation and robust clustering by Jiang Luo, Lingjun Liu

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