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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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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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…A pre-processed and augmented dataset is used for training of the convolutional neural network having an architecture with hierarchical structure and residual block usage. …”
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PoseAlign network for hybrid structure in 2D human pose estimation by Jin Zhang, Yabo Yin, Wenzhong Yang, Doudou Ren, Danny Chen

“…We introduce a novel 2D HPE method called the PoseAlign Network for Hybrid Structure (PAN-HS). PAN-HS leverages the conceptually simple yet effective depth-wise convolution to design two feature extraction blocks: the Spatial Align Block and the Channel Align Block. …”
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Infant cry classification using an efficient graph structure and attention-based model by Qiao X., Jiao S., Li H.

“…Additionally, in order to better classify the efficient graph structure, a local-to-global convolutional neural network (AlgNet) based on convolutional neural networks and attention mechanisms is proposed. …”
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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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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 task of PolSAR image classification, effectively utilizing convolutional neural networks and vision transformer models with limited labeled data poses a critical challenge. …”
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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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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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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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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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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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SAR ship target detection method based on CNN structure with wavelet and attention mechanism. by Shiqi Huang, Xuewen Pu, Xinke Zhan, Yucheng Zhang, Ziqi Dong, Jianshe Huang

“…The new method uses the U-Net structure to construct the network, which not only effectively reduces the depth of the network structure, but also significantly improves the complexity of the network. …”
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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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