GP-GCN: Global features of orthogonal projection and local dependency fused graph convolutional networks for aspect-level sentiment classification by Subo Wei, Guangli Zhu, Zhengyan Sun, Xiaoqing Li, TienHsiung Weng

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Removing Stripe Noise From Infrared Cloud Images via Deep Convolutional Networks by Pengfei Xiao, Yecai Guo, Peixian Zhuang

“…To further improve the performance, we propose a local-global combination structure model, which combines the representations of different layers for recovering the rich details of infrared cloud images. …”
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MolNexTR: a generalized deep learning model for molecular image recognition by Yufan Chen, Ching Ting Leung, Yong Huang, Jianwei Sun, Hao Chen, Hanyu Gao

“…Abstract In the field of chemical structure recognition, the task of converting molecular images into machine-readable data formats such as SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent in chemical literature. …”
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Efficient Semantic Segmentation of Remote Sensing Images Through Global-Local Feature Integration by Fengyi Zhang, Xiuyu Xia

“…To address these challenges, this paper proposes an efficient remote sensing image semantic segmentation model called Multi-GLISS, which integrates global and local features. The model captures global features through consecutive downsampling and Fourier transform while preserving spatial feature learning and boundary information using convolutional residual layers. …”
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MSGEGA: Multiscale Gaussian Enhancement and Global-Aware Network for Infrared Small Target Detection by Yuyang Xi, Liuwei Zhang, Ying Jiang, Feng Qian, Fanjiao Tan, Qingyu Hou

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Speech Emotion Recognition Using Multi-Scale Global–Local Representation Learning with Feature Pyramid Network by Yuhua Wang, Jianxing Huang, Zhengdao Zhao, Haiyan Lan, Xinjia Zhang

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GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search by Yi Wang, Feng Zhan, Cuiyu Huang, Yiran Huang

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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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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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CCDR: Combining Channel-Wise Convolutional Local Perception, Detachable Self-Attention, and a Residual Feedforward Network for PolSAR Image Classification by Jianlong Wang, Bingjie Zhang, Zhaozhao Xu, Haifeng Sima, Junding Sun

“…In the channel-wise convolutional local perception module, channel-wise convolution operations enable accurate extraction of local features from different channels of PolSAR images. …”
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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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GLN-LRF: global learning network based on large receptive fields for hyperspectral image classification by Mengyun Dai, Tianzhe Liu, Youzhuang Lin, Zhengyu Wang, Yaohai Lin, Changcai Yang, Riqing Chen

“…Future work will further optimize the model structure, enhance computational efficiency, and explore its application potential in other types of remote sensing data.…”
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gamUnet: designing global attention-based CNN architectures for enhanced oral cancer detection and segmentation by Jinyang Zhang, Hongxin Ding, Hongxin Ding, Runchuan Zhu, Weibin Liao, Weibin Liao, Junfeng Zhao, Junfeng Zhao, Min Gao, Xiaoyun Zhang

“…Traditional CNNs which sturggle to capture critical global contextual information often fail to distinguish the complex tissue structures in OSCC images.MethodsTo address these challenges, we propose a novel architecture called gamUnet, which integrates the Global Attention Mechanism (GAM) to enhance the model's ability to capture global cross-modal information. …”
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Lightweight interactive feature inference network for single-image super-resolution by Li Wang, Xing Li, Wei Tian, Jianhua Peng, Rui Chen

“…SAAB adaptively recalibrates local salient structural information, and SWTB effectively captures rich global information. …”
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Interesting Concept Mining With Concept Lattice Convolutional Networks by Mohamed Hamza Ibrahim, Rokia Missaoui, Pedro Henrique B. Ruas

“…In this paper, we introduce the Concept Lattice Convolutional Network (<inline-formula> <tex-math notation="LaTeX">$\mathcal {LCN}$ </tex-math></inline-formula>), an efficient semi-supervised learning approach to identify actionable concepts (i.e., interesting conceptual structures) based on a scalable convolutional neural network architecture that operates on concept lattices. …”
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On the convolutive development of elastic substrate media as nano foundation by D. Indronil, IM Nazmul

“…The validity of the proposed model is verified through comparisons with established theories, demonstrating its precision and broader applicability to complex structural scenarios. The convolution-based formulation also enhances the analysis of advanced loading conditions and nonlinear material responses, making it highly adaptable to real-world engineering applications. …”
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Target Tracking via Particle Filter and Convolutional Network by Hongxia Chu, Kejun Wang, Xianglei Xing

“…The global representation is generated by combining local features without changing their structures and space arrangements. …”
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An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory by Yanbo Sun, Wenxing Bao, Wei Feng, Kewen Qu, Xuan Ma, Xiaowu Zhang

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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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Parking space number detection with multi‐branch convolution attention by Yifan Guo, Jianxun Zhang, Yuting Lin, Jie Zhang, Bowen Li

“…Since no scholar has proposed a high‐performance method for such problems, a parking space number detection model based on the multi‐branch convolutional attention is presented. Firstly, using ResNet50 as the backbone network, a multi‐branch convolutional structure is proposed in the backbone network, which aims to process and fuse the feature map through three parallel branches, and enhance the network to represent ability information by convolutional attention, learn global features to selectively strengthen the features containing helpful information, and improve the ability of the model to detect the parking space number area. …”
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