A high-precision edge detection technique for magnetic anomaly signals based on a self-attention mechanism by Ju Haihua, Wang Li, Yang Jie, Liu Gaochuan, Xia Zhong, Jiao Jian, Zhang Le, Dai Bo

“…Magnetic data boundary detection is a key technology in potential field data processing, providing an effective basis for the division of geological units and fault structures. It holds significant importance in geological structure analysis and mineral exploration. …”
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AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder by Yunseung Lee, Pilsung Kang

“…These methods are actively used in various fields such as manufacturing, medical care, and intelligent information. Encoder-decoder structures have been widely used in the field of anomaly detection because they can easily learn normal patterns in an unsupervised learning environment and calculate a score to identify abnormalities through a reconstruction error indicating the difference between input and reconstructed images. …”
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CGFTNet: Content-Guided Frequency Domain Transform Network for Face Super-Resolution by Yeerlan Yekeben, Shuli Cheng, Anyu Du

“…Recent advancements in face super resolution (FSR) have been propelled by deep learning techniques using convolutional neural networks (CNN). However, existing methods still struggle with effectively capturing global facial structure information, leading to reduced fidelity in reconstructed images, and often require additional manual data annotation. …”
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Research on Vehicle Target Detection Method Based on Improved YOLOv8 by Mengchen Zhang, Zhenyou Zhang

“…A Lightweight Shared Convolution Detection Head was designed. By designing a shared convolution layer through group normalization, the detection head of the original model was improved, which can reduce redundant calculations and parameters and enhance the ability of global information fusion between feature maps, thereby achieving the purpose of improving computational efficiency. …”
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MSAmix-Net: Diabetic Retinopathy Classification by Jianyun Gao, Shu Li, Yiwen Chen, Rongwu Xiang

“…Most models are based on convolutional neural networks, but due to the small size of convolution kernels in shallow networks, the receptive field is limited, preventing the capture of global information. …”
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Lightweight human activity recognition method based on the MobileHARC model by Xingyu Gong, Xinyang Zhang, Na Li

“…However, due to the fact that these models have sequential network structures and are unable to simultaneously focus on local and global features, thus, resulting in a reduction in recognition performance. …”
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Efficient Image Super-Resolution With Multi-Branch Mixer Transformer by Long Zhang, Yi Wan

“…To address these problems, we propose a Multi-Branch Token Mixer (MBTM) to extract richer global and local information. Compared to other Transformer-based SR networks, MBTM achieves a balance between capturing global information and reducing the computational complexity of self-attention through its compact multi-branch structure. …”
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Improved Asynchronous Federated Learning for Data Injection Pollution by Aiyou Li, Huoyou Li, Yanfang Liu, Guoli Ji

“…In our approach, the residual network is used to extract the static information of the image, the capsule network is used to extract the spatial dependence among the internal structures of the image, several layers of convolution are used to reduce the dimensions of both features, and the two extracted features are fused. …”
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Distributed Photovoltaic Short-Term Power Prediction Based on Personalized Federated Multi-Task Learning by Wenxiang Luo, Yang Shen, Zewen Li, Fangming Deng

“…By improving the parallel pooling structure of a time series convolution network (TCN), an improved time series convolution network (iTCN) prediction model was established, and the channel attention mechanism CBAMANet was added to highlight the key meteorological characteristics’ information and improve the feature extraction ability of time series data in photovoltaic power prediction. …”
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YOLO-HVS: Infrared Small Target Detection Inspired by the Human Visual System by Xiaoge Wang, Yunlong Sheng, Qun Hao, Haiyuan Hou, Suzhen Nie

“…Meanwhile, the C2f_DWR (dilation-wise residual) module with regional-semantic dual residual structure is designed to significantly improve the efficiency of capturing multi-scale contextual information by expanding convolution and two-step feature extraction mechanism. …”
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Sa-SNN: spiking attention neural network for image classification by Yongping Dan, Zhida Wang, Hengyi Li, Jintong Wei

“…The design of local inter-channel interactions through adaptive convolutional kernel sizes, rather than global dependencies, allows the network to focus more on the selection of important features, reduces the impact of redundant features, and improves the network’s recognition and generalisation capabilities. …”
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A small object detection model in aerial images based on CPDD-YOLOv8 by Jingyang Wang, Jiayao Gao, Bo Zhang

“…Firstly, we propose the C2fGAM structure, which integrates the Global Attention Mechanism (GAM) into the C2f structure of the backbone so that the model can better understand the overall semantics of the images. …”
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Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention by Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang

“…Recently, graph-based methods have also been used to predict trajectories, however processing graph-structured data introduces significant increase in computation. …”
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Attention residual network for medical ultrasound image segmentation by Honghua Liu, Peiqin Zhang, Jiamin Hu, Yini Huang, Shanshan Zuo, Lu Li, Mailan Liu, Chang She

“…Additionally, a spatial hybrid convolution module is integrated to augment the model’s ability to extract global information and deepen the vertical architecture of the network. …”
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Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion by Jingru Wang, Shipeng Wen, Wenjie Liu, Xianglian Meng, Zhuqing Jiao

“…The other branch learned the position information of brain regions with different changes in the different categories of subjects’ brains by introducing attention convolution, and then obtained the discriminative probability information from locations via convolution and global average pooling. …”
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A New Hybrid ConvViT Model for Dangerous Farm Insect Detection by Anil Utku, Mahmut Kaya, Yavuz Canbay

“…This study proposes a novel hybrid convolution and vision transformer model (ConvViT) designed to detect harmful insect species that adversely affect agricultural production and play a critical role in global food security. …”
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A Malware Classification Method Based on Knowledge Distillation and Feature Fusion by Xin Guan, Guodong Zhang

“…This approach incorporates image texture features with enhanced Local Binary Pattern (LBP), providing insights into the local structure and layout of images and aiding the model in better understanding image details and internal structure, thus enhancing classification performance. …”
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TMAR: 3-D Transformer Network via Masked Autoencoder Regularization for Hyperspectral Sharpening by Zeinab Dehghan, Jingxiang Yang, Mehran Yazdi, Abdolraheem Khader, Liang Xiao

“…In this study, we focus on leveraging the power of CNN and transformer models and propose a multistage deep transformer-based super-resolution network that is regularized via an asymmetric autoencoder structure. In addition, we utilize a 3-D convolution layer in the light transformer structure because it allows for more flexible computation of correlations between HSI layers and better capturing of dependencies within spectral–spatial features. …”
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Remote sensing image Super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer by Denghui Liu, Lin Zhong, Haiyang Wu, Songyang Li, Yida Li

“…The discriminator combines multi-scale convolution, global Transformer, and hierarchical feature discriminators, providing a comprehensive and refined evaluation of image quality. …”
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MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph by Yinfei Dai, Yuantong Zhang, Xiuzhen Zhou, Qi Wang, Xiao Song, Shaoqiang Wang

“…In addition, we utilize the graph convolutional neural network (GCN) to process graph-structured data. …”
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