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. …”
Get full text

Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networks by Xiujuan Zeng, Tong Liu, Huiqin Xie, Dajiang Wang, Jihong Xiao

“…To address the challenge of extracting complex nonlinear features from multivariate electrical data, a combined model of a self-attention mechanism and a graph convolutional network (GCN) is proposed. The self-attention mechanism captures global dependencies among power parameters, while the GCN effectively constructs the multivariate data structures in distribution networks. …”
Get full text

A Lightweight Single-Image Super-Resolution Method Based on the Parallel Connection of Convolution and Swin Transformer Blocks by Tengyun Jing, Cuiyin Liu, Yuanshuai Chen

“…Specifically, through a parallel structure of channel feature-enhanced convolution and Swin Transformer, the network extracts, enhances, and fuses the local and global information. …”
Get full text

Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network by Hongliang Zhu, Hongxi Zhao, Chunshan Bao, Yiran Shi, Wenchao He

“…The C-GNN architecture combines 1D convolutional layers for local spatial feature extraction with graph convolutional layers for global structural learning, effectively capturing both fine-grained and long-range array dependencies. …”
Get full text

HMCFormer (hierarchical multi-scale convolutional transformer): a hybrid CNN+Transformer network for intelligent VIA screening by Bo Feng, Chao Xu, Zhengping Li, Chuanyi Zhang

“…The authors apply the structure of the Swin Transformer network with minor modifications in the global perception modeling process. …”
Get full text

ADMNet: adaptive deformable convolution large model combining multi-level progressive fusion for Building Change Detection by Liye Mei, Haonan Yu, Zhaoyi Ye, Chuan Xu, Cheng Lei, Wei Yang

“…Nevertheless, the mainstream detection methods utilizing traditional convolution and attention mechanisms are often prone to errors due to the loss of edge detail information and underutilization of global context information. …”
Get full text

Seismic data denoising based on attention dual dilated CNN by Haixia Hu, Youhua Wei, Hui Chen, Xingan Fu, Ji Zhang, Quan Wang, Shiwei Cai

“…Traditional noise suppression methods often result in the loss of critical signals, affecting subsurface structure characterization. This study introduces an innovative Attention Dual-Dilated Convolutional Neural Network (ADDC-Net) to address random noise in seismic data. …”
Get full text

Multiscale Feature Fusion for Salient Object Detection of Strip Steel Surface Defects by Li Zhang, Xirui Li, Yange Sun, Yan Feng, Huaping Guo

“…Experimental results show that, compared with state-of-the-art methods, the proposed method achieves the best performance in terms of mean absolute error, structural similarity, weighted F-measure and Pratt’s figure of merit. …”
Get full text

Category semantic and global relation distillation for object detection by Yanpeng LIANG, Zhonggui MA, Zongjie WANG, Zhuo LI

“…Knowledge distillation stands out as it transfers knowledge from large teacher models to compact student models without modifying the network structure, enabling the student models to perform nearly as well as their larger counterparts. …”
Get full text

Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention by Huimin Qu, Haiyan Xie, Qianying Wang

“…The overall network structure contains four parts: a global sparse network (GSN), a dense residual attention network (DRAN), a feature distiller network (FDN), and a feature processing block (FPB). …”
Get full text

MolAttnNet: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention by Chenxu Wang, Yijun Feng, Zhejie Xu, Xiaohui Xu, Bangguo Peng

“…The framework comprises three specialized modules: a Graph Convolutional Network for extracting local molecular structural features, a multi-granularity attention mechanism for capturing both local and global molecular dependencies, and an adaptive LSTM with chemically-informed forget gates for selective feature retention and noise attenuation. …”
Get full text

Integrating Multiscale Spatial–Spectral Shuffling Convolution With 3-D Lightweight Transformer for Hyperspectral Image Classification by Qinggang Wu, Mengkun He, Qiqiang Chen, Le Sun, Chao Ma

“…The combination of convolutional neural networks and vision transformers has garnered considerable attention in hyperspectral image (HSI) classification due to their abilities to enhance the classification accuracy by concurrently extracting local and global features. …”
Get full text

An object detection model AAPW-YOLO for UAV remote sensing images based on adaptive convolution and reconstructed feature fusion by Yiming Wu, Xiaofang Mu, Hong Shi, Mingxing Hou

“…To overcome these challenges, this paper presents a model for detecting small objects, AAPW-YOLO, based on adaptive convolution and reconstructed feature fusion. In the AAPW-YOLO model, we improve the standard convolution and the CSP Bottleneck with 2 Convolutions (C2f) structure in the You Only Look Once v8 (YOLOv8) backbone network by using Alterable Kernel Convolution (AKConv), which improves the network’s proficiency in capturing features across various scales while considerably lowering the model’s parameter count. …”
Get full text

Multi-frequency EEG and multi-functional connectivity graph convolutional network based detection method of patients with Alzheimer’s disease by Yujian Liu, Libing An, Haiqiang Yang, Shuzhi Sam Ge

“…This network comprehensively captures abnormalities in brain network structures induced by AD, across different frequency bands and connectivity modes. …”
Get full text

WT-HMFF: Wavelet Transform Convolution and Hierarchical Multi-Scale Feature Fusion Network for Detecting Infrared Small Targets by Siyu Li, Jingsi Huang, Qingwu Duan, Zheng Li

“…WTConv expands the receptive field through wavelet convolution, effectively capturing global contextual information while preserving target shape characteristics. …”
Get full text

Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks by Muhammad Amir Khan, Tehseen Mazhar, Muhammad Danish Ali, Umar Farooq Khattak, Tariq Shahzad, Mamoon M. Saeed, Habib Hamam

“…Example: “Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. …”
Get full text

Background-Supported Global Feature Response Image Classification Network by JIANG Wentao, LI Weida, ZHANG Shengchong

“…Then, a full-domain feature response module BGR (background-supported global feature response) is proposed, and BGR is embedded into the residual branch to restore the image full domain features, which reduces the loss of feature information due to the convolution operation to a certain extent. …”
Get full text

Application of 3D ELD_MobileNetV2 Incorporating Attention Mechanism and Dilated Convolution in Hepatic Nodules Classification by SUN Haoyun, WANG Lijia

“…Then, 3D dilated structure was introduced into depthwise convolution to improve the receptive field of the convolution kernel. …”
Get full text

Combining convolutional neural network with transformer to improve YOLOv7 for gas plume detection and segmentation in multibeam water column images by Wenguang Chen, Xiao Wang, Junjie Chen, Jialong Sun, Guozhen Zha

“…First, we sequentially reduce the ELAN (Efficient Layer Aggregation Networks) structure in the backbone network and verify that using the enhanced feature extraction module only in the deep network is more effective in recognising the gas plume targets. …”
Get full text

A Spatio-Temporal Joint Diagnosis Framework for Bearing Faults via Graph Convolution and Attention-Enhanced Bidirectional Gated Networks by Zhiguo Xiao, Xinyao Cao, Huihui Hao, Siwen Liang, Junli Liu, Dongni Li

“…This architecture achieves the deep fusion of spatio-temporal features through the graph-structural transformation of vibration signals and a feature cascading strategy, thereby improving overall model performance. …”
Get full text