SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture by Taixing Qiu, Jiawei Li, Yan Guo, Limin Jiang, Jijun Tang

“…Additionally, SVEA integrates multi-head self-attention mechanisms and multi-scale convolution modules, enhancing its ability to capture global context and multi-scale features. …”
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Parking space number detection with multi‐branch convolution attention by Yifan Guo, Jianxun Zhang, Yuting Lin, Jie Zhang, Bowen Li

“…Since no scholar has proposed a high‐performance method for such problems, a parking space number detection model based on the multi‐branch convolutional attention is presented. Firstly, using ResNet50 as the backbone network, a multi‐branch convolutional structure is proposed in the backbone network, which aims to process and fuse the feature map through three parallel branches, and enhance the network to represent ability information by convolutional attention, learn global features to selectively strengthen the features containing helpful information, and improve the ability of the model to detect the parking space number area. …”
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Fault Diagnosis of Rotating Machinery Based on Evolutionary Convolutional Neural Network by Yihao Bai, Weidong Cheng, Weigang Wen, Yang Liu

“…This paper proposes a fault diagnosis method for rotating machinery based on evolutionary convolutional neural network (ECNN). With the time-frequency images as the network input, with the help of the global optimization ability of the genetic algorithm, the structure of the convolutional neural network can evolve autonomously, and the adaptive configuration of the structural hyperparameters for the target task is realized. …”
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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. …”
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A point cloud segmentation network with hybrid convolution and differential channels by Xiaoyan Zhang, Yantao Bu

“…Specifically, we design a hybrid convolutional feature extraction (HCFE) module for processing 3D semantic information and spatial information independently, using different convolution kernels to obtain the subtle geometric structure differences between points. …”
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SMS spam detection using BERT and multi-graph convolutional networks by Linjie Shen, Yanbin Wang, Zhao Li, Wenrui Ma

“…This multigraph approach captures diverse features and models both global and local structures using tailored Graph Convolutional Networks. …”
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D3GNN: Double dual dynamic graph neural network for multisource remote sensing data classification by Teng Yang, Song Xiao, Jiahui Qu

“…However, classical CNN-based methods primarily concentrate on information within a fixed-size neighborhood and a standard square region, neglecting long-range and global information. As non-Euclidean data, the topological structure enables flexible construction of relationships between objects, which can be served as an effective carrier of global information. …”
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TIER: Temporal Convolutional Network Information Extractor With Conditional Random Field by Huiwen Wu, Xiubo Zhang, Tengyuan Zhou, An Hu

“…The dilated convolution structure of TCN is used to expand the Receptive Fields (RFs) to capture long-distance context features, and the global dependency between labels is modeled through the CRF layer to improve the information extraction performance further. …”
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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. …”
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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. …”
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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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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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U-Shaped Dual Attention Vision Mamba Network for Satellite Remote Sensing Single-Image Dehazing by Tangyu Sui, Guangfeng Xiang, Feinan Chen, Yang Li, Xiayu Tao, Jiazu Zhou, Jin Hong, Zhenwei Qiu

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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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