Cross-stream attention enhanced central difference convolutional network for CG image detection by HUANG Jinkun, HUANG Yuanhang, HUANG Wenmin, LUO Weiqi

“…Vanilla convolutional layers in each stream were replaced by central difference convolutions, which allowed the model to simultaneously extract pixel intensity information and pixel gradient information from the image. …”
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Multi-Scale Residual Convolutional Neural Network with Hybrid Attention for Bearing Fault Detection by Yanping Zhu, Wenlong Chen, Sen Yan, Jianqiang Zhang, Chenyang Zhu, Fang Wang, Qi Chen

“…The multi-scale residual network structure captures features at various scales, and fault classification is performed using global average and max pooling. …”
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Crack-ConvT Net: A Convolutional Transformer Network for Crack Segmentation in Underwater Dams by Pengfei Shi, Hongzhu Chen, Zaiming Geng, Xinnan Fan, Yuanxue Xin

“…Abstract Crack detection is a critical approach to ensuring the structural health of dams. However, challenges like uneven underwater lighting, sediment interference, and complex backgrounds often hinder traditional detection methods, leading to feature loss and false detections. …”
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3DVT: Hyperspectral Image Classification Using 3D Dilated Convolution and Mean Transformer by Xinling Su, Jingbo Shao

“…Furthermore, traditional convolutional neural networks (CNNs) primarily focus on local features in hyperspectral data, neglecting long-range dependencies and global context. …”
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MixRformer: Dual-Branch Network for Underwater Image Enhancement in Wavelet Domain by Jie Li, Lei Zhao, Heng Li, Xiaojun Xue, Hui Liu

“…An innovative dual-branch feature capture module (DFCB) is designed as follows: (1) the surface information extraction block combines convolution and position encoding to enhance local modeling; (2) the rectangular window gated Transformer expands the receptive field through the convolution gating mechanism to achieve efficient global relationship modeling. …”
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Deep convolutional neural network for quantification of tortuosity factor of solid oxide fuel cell anode by Masashi KISHIMOTO, Yodai MATSUI, Hiroshi IWAI

“…In addition, since the DCNN model is designed to analyze 3D structures with arbitrary sizes in each dimension by adopting the global average pooling layer, its estimation accuracy for analyzing structures with different volumes is investigated. …”
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Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction by Lei Huang, Jianxin Qin, Tao Wu

“…Specifically, it extracts different types of traffic flows from multiple data sources and constructs a unified graph structure by using global traffic nodes to interpolate the traffic flow. …”
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UnetTransCNN: integrating transformers with convolutional neural networks for enhanced medical image segmentation by Yi-Hang Xie, Bo-Song Huang, Fan Li, Fan Li

“…Traditional CNN-based methods effectively capture local features but struggle with modeling global contextual dependencies. Recently, transformer-based models have shown promise in capturing long-range information; however, their integration with CNNs remains suboptimal in many hybrid approaches.MethodsWe propose UnetTransCNN, a novel parallel architecture that combines the strengths of Vision Transformers (ViT) and Convolutional Neural Networks (CNNs). …”
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Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

“…Skeleton-sequence-based behavior recognition models are characterized by fast processing speeds, low computational requirements, and simple structures. Graph convolutional networks (GCNs) have advantages in processing skeleton sequence data. …”
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A Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation for Bearing Fault Diagnosis by Jiamao Yu, Hexuan Hu

“…Stage 1 uses wide-kernel convolution for initial feature extraction, while Stages 2 through 5 integrate a parallel multi-scale convolutional structure to capture both global contextual information and long-range dependencies. …”
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MSASGCN :  Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting by Yang Cao, Detian Liu, Qizheng Yin, Fei Xue, Hengliang Tang

“…It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. …”
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Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks by Lei Zhang, Xiangqian Ding, Ruichun Hou

“…Secondly, the one-dimensional NIRS data are converted into two-dimensional spectral images, and the structure features are extracted from two-dimensional spectral images by the two-dimensional convolutional neural network (2-D CNN) method. …”
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Rolling Bearing Life Prediction Based on Improved Transformer Encoding Layer and Multi-Scale Convolution by Zhuopeng Luo, Zhihai Wang, Xiaoqin Liu, Yingming Yang

“…A novel dual-layer self-attention mechanism network structure is proposed to capture global information on the lifecycle progression of rolling bearings. …”
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DMCCT: Dual-Branch Multi-Granularity Convolutional Cross-Substitution Transformer for Hyperspectral Image Classification by Laiying Fu, Xiaoyong Chen, Yanan Xu, Xiao Li

“…In the field of hyperspectral image classification, deep learning technology, especially convolutional neural networks, has achieved remarkable progress. …”
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Multi-classification of breast cancer histopathological image using enhanced shallow convolutional neural network by Musa Yusuf, Armand Florentin Donfack Kana, Mustapha Aminu Bagiwa, Mohammed Abdullahi

“…Abstract Cancer of the breast popularly known as breast cancer (BC) is the second and third utmost cause of mortality among women in Nigeria and globally, respectively. Biopsy histopathological images (BHI) have gained more attention for the early clinical diagnosis of BC. …”
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CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer by Kai Liu, Tao Ren, Zhangli Lan, Yang Yang, Rong Liu, Yuantong Xu

“…By fostering information exchange among local features, the model enhances comprehension of the global structural patterns of cracks and improves inference capabilities in recognizing intricate crack configurations. …”
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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

“…To alleviate these issues, we propose a global learning network with a large receptive fields network (GLNet) to capture more comprehensive and accurate global contextual information, thereby enriching the underlying feature representation for hyperspectral image classification. …”
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Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution by Xiongtao ZHANG, Jingyu ZHENG, Qing SHEN, Danfeng SUN, Yunliang JIANG

“…Aiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency, a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution (MHGCN) was proposed.A sandwich structure (i.e.multi-channel spatial module in the middle and temporal module on both sides) was used in the model to extract spatial-temporal features, and the multi-channel spatial module was divided into static graph convolution module and dynamic graph convolution module.The static graph convolution module simultaneously extracted specific and common features from topological spatial structures, semantic spatial structures, and their combinations.The dynamic graph convolution module assigned different weights to different features and extracts dynamic spatial features from unknown graph structures.In the temporal module, the multi-head attention mechanism was used to extract the global temporal features, and the temporal gating mechanism extracted the local temporal features.The model extracted spatial information from different spatial structures and temporal information from different time intervals to establish a global and comprehensive spatial-temporal relationship.The experimental results show that the MHGCN performs better than the existing traffic flow prediction models on four real world traffic flow datasets.…”
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Analysis of China’s High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks by Zhenguo Xu, Jun Li, Irene Moulitsas, Fangqu Niu

“…This study investigated the characteristics and functionalities of China’s High-Speed Railway (HSR) network based on Complex Network Theory (CNT) and Graph Convolutional Networks (GCN). First, complex network analysis was applied to provide insights into the network’s fundamental characteristics, such as small-world properties, efficiency, and robustness. …”
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DEDSWIN-Net: Dual Encoder Dilated Convolution and Swin Transformer Network for the Classification of Liver CT Images by Jyoshna Allenki, Hemant Kumar Soni

“…The dilated convolution layers can retrieve fine spatial features, whereas Swin models are designed to obtain global information. …”
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