HEAT: Incorporating hierarchical enhanced attention transformation into urban road detection by Ting Han, Chuanmu Li, Siyu Chen, Zongyue Wang, Jinhe Su, Yundong Wu, Guorong Cai

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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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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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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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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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Classification Modeling Method for Near-Infrared Spectroscopy of Tobacco Based on Multimodal Convolution Neural Networks by Lei Zhang, Xiangqian Ding, Ruichun Hou

“…The classification is performed by the combination of global and local training features. Finally, the influences of different network structure parameters on model identification performance are studied, and the optimal CNN models are selected and compared. …”
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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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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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Glaucoma detection from retinal fundus images using graph convolution based multi-task model by Satyabrata Lenka, Zefree Lazarus Mayaluri, Ganapati Panda

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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

“…The community structures identified by the integrated GCN model highlight the economic and social connections between official urban clusters and the communities. …”
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Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction by Chuan Zhao, Xin Li, Zezhi Shao, HongJi Yang, Fei Wang

“…To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. Temporal connections are learned from both the local and global information in a time-series sequence using the combination of a time-trend feature mapping block and a gated recurrent unit block. …”
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Wi-Fi-Enabled Vision via Spatially-Variant Pose Estimation Based on Convolutional Transformer Network by Hyeon-Ju Lee, Seok-Jun Buu

“…To address these challenges, we propose a Convolutional Transformer Network. This architecture integrates convolutional layers for localized spatial feature extraction and transformer layers for global temporal dependency modeling. …”
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Research on agricultural disease recognition methods based on very large Kernel convolutional network-RepLKNet by Guoquan Pei, Xueying Qian, Bing Zhou, Zigao Liu, Wendou Wu

“…However, conventional convolutional neural networks that rely on multi-layer small-kernel structures are limited in capturing long-range dependencies and global contextual information due to their constrained receptive fields. …”
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CCTNet: CNN and Cross-Shaped Transformer Hybrid Network for Remote Sensing Image Semantic Segmentation by Honglin Wu, Zhaobin Zeng, Peng Huang, Xinyu Yu, Min Zhang

“…This model follows an encoder–decoder structure. It employs ResNet18 as an encoder to extract hierarchical feature information, and constructs a transformer decoder based on efficient cross-shaped self-attention to fully model local and global feature information and achieve lightweighting of the network. …”
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Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism by Zhichao Li, Gang Li, Ling Zhang, Guijuan Cheng, Shan Wu

“…Aiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution, a network with parallel two-branch structure is proposed. In the encoder, the Pyramid Pooling Transformer (P2T) backbone is used as the Transformer branch to obtain the global features of the lesions, the convolutional branch is used to extract the lesions’ local feature information, and the feature fusion module is designed to effectively fuse the features in the dual branches; subsequently, in the decoder, the channel prior convolutional attention is used to enhance the localization ability of the model to the lesion region. …”
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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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TCN-SA: A Social Attention Network Based on Temporal Convolutional Network for Vehicle Trajectory Prediction by Qin Li, Bingguang Ou, Yifa Liang, Yong Wang, Xuan Yang, Linchao Li

“…To address this issue, we propose a hybrid deep learning model based on a temporal convolutional network (TCN) that considers local and global interactions between vehicles. …”
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Bearing fault diagnosis for variable operating conditions based on KAN convolution and dual branch fusion attention by Qibing Wang, Chuanjie Yin, Kun She, Qinfeng Tong, Guoxiong Lu, Hongbing Zhang, Jiawei Lu

“…Firstly, a new structure of KANs is applied to Convolutional Neural Networks (CNN) for replacing traditional linear convolutional kernels. …”
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