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.…”
Get full text

Action recognition using part and attention enhanced feature fusion by Danfeng Zhuang, Min Jiang, Lei Wang, Mohamed Sanim Akremi, Hedi Tabia

Get full text

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. …”
Get full text

Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction by Juan Chen, Rui Huang

“…The model utilizes a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively. …”
Get full text

Global information aware network with global interaction graph attention for infrared small target detection by Ruimin Yang, Yidan Zhang, Guangshuai Gao, Liang Liao, Chunlei Li

“…However, distinguishing small infrared targets from similar backgrounds is challenging due to their lack of structural and textural characteristics. To address these challenges, this study proposes a novel global information‐aware network with global interaction graph attention (GIGA) for infrared small target detection. …”
Get full text

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

Get full text

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. …”
Get full text

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. …”
Get full text

A Novel Hierarchical Multimodal Recommender With Enhanced Global Collaborative Signals by Peng Yi, Lu Chen, Zhaoxian Li, Cheng Yang

“…Specifically, modality features are first utilized to identify neighboring relationships, and similar users (items) are steadily merged together to form modality-specific hierarchical structures. Then, with the proper graph convolution operation on each hierarchy, the crucial global collaborative signals can be effectively extracted and integrated into the modality-specific user (item) embeddings. …”
Get full text

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.…”
Get full text

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

Get full text

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

Get full text

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

Get full text

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. …”
Get full text

Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention by Jianjianxian Liu, Tao Xing, Xiangyu Wang

“…The multi-branch convolutional module extracts diverse features by processing input with branches of different kernel sizes, capturing both fine and global details. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases by Tianyue Jiang, Xu Du, Ning Zhang, Xiuhan Sun, Xiao Li, Siqing Tian, Qiuyan Liang

“…Additionally, Omni-dimensional Dynamic Convolution was employed to optimize the model’s basic convolutional structure, bottleneck structure, and C2f (Faster Implementation of CSP (Cross Stage Partial) Bottleneck with two convolutions) module, improving feature fusion quality and reducing computational complexity. …”
Get full text

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. …”
Get full text