Multi-scale convolutional transformer network for motor imagery brain-computer interface by Wei Zhao, Baocan Zhang, Haifeng Zhou, Dezhi Wei, Chenxi Huang, Quan Lan

“…The multi-branch multi-scale CNN structure effectively addresses individual variability in EEG signals, enhancing the model’s generalization capabilities, while the Transformer encoder strengthens global feature integration and improves decoding performance. …”
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Optimization of the road bump and pothole detection technology using convolutional neural network by Ding Haiping, Tang Qianlong

“…In addition, this work explores the combination of sensor fusion techniques, combining data from many sources such as bridge structural health monitoring systems, cameras, accelerometers, and Global Positioning System. …”
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3D long time spatiotemporal convolution for complex transfer sequence prediction by Qiu Yunan, Cui Yingjie, Tang Haibo, Chen Zhongfeng, Lu Zhenyu, Xue Feng

“…Secondly, a cross-structured spatio-temporal attention module is constructed based on spatio-temporal features in the decoding stage to enhance the response of fine features in the image in the convolutional channel, so as to capture non-smooth local features. …”
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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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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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TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting by Xiaxia He, Wenhui Zhang, Xiaoyu Li, Xiaodan Zhang

“…Specifically, we design an adaptive graph convolutional module to dynamically capture implicit road dependencies at different time levels and a local-global temporal attention module to simultaneously capture long-term and short-term temporal dependencies. …”
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Incorporating convolutional and transformer architectures to enhance semantic segmentation of fine-resolution urban images by Xizi Yu, Shuang Li, Yu Zhang

“…Though convolutional neural networks (CNN) exhibit promise in image semantic segmentation, they have limitations in capturing global context information, resulting in inaccuracies in segmenting small object features and object boundaries. …”
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Aspect-Based Sentiment Analysis Through Graph Convolutional Networks and Joint Task Learning by Hongyu Han, Shengjie Wang, Baojun Qiao, Lanxue Dang, Xiaomei Zou, Hui Xue, Yingqi Wang

“…Next, GCN models the graph structure of the input data, capturing the relationships between nodes and global structural information, fully integrating global contextual semantic information, and generating deep-level contextual feature representations. …”
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Cross-stream attention enhanced central difference convolutional network for CG image detection by HUANG Jinkun, HUANG Yuanhang, HUANG Wenmin, LUO Weiqi

“…A dual-stream structure was constructed in the model, in order to extract semantic features and non-semantic residual texture features 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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Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An, Yongjun Xu

“…This method integrates similarity and gradient information and employs graph convolution to capture the global contextual relationships among features. …”
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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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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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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

“…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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