BiEHFFNet: A Water Body Detection Network for SAR Images Based on Bi-Encoder and Hybrid Feature Fusion by Bin Han, Xin Huang, Feng Xue

“…First, a bi-encoder structure based on ResNet and Swin Transformer is used to jointly extract local spatial details and global contextual information, enhancing feature representation in complex scenarios. …”
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Bitemporal Remote Sensing Change Detection With State-Space Models by Lukun Wang, Qihang Sun, Jiaming Pei, Muhammad Attique Khan, Maryam M. Al Dabel, Yasser D. Al-Otaibi, Ali Kashif Bashir

“…Change detection in very-high-resolution remote sensing images has gained significant attention, particularly with the rise of deep learning techniques such as convolutional neural networks and Transformers. The Mamba structure, successful in computer vision, has been applied to this domain, enhancing computational efficiency. …”
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Research on SeaTreasure Target Detection Technology Based on Improved YOLOv7-Tiny by Xiang Shi, Yunli Zhao, Jinrong Guo, Yan Liu, Yongqi Zhang

“…First, based on the YOLOv7-Tiny network, the MAFPN neck structure is used to replace the ELAN structure to achieve the multi-scale capture of semantic information of underwater sea treasures, and to enhance the UPA-YOLO model to accurately locate the targets of underwater sea treasures; second, the P2ELAN module is constructed and added to the backbone network, which makes use of the redundancy information in the feature map and dynamically adjusts the convolution kernel to adapt to data The P2ELAN module is added to the backbone network, using the redundant information in the feature map, dynamically adjusting the convolutional kernel to adapt to the lack of data, reducing the number of parameters in the model, and introducing the MSCA attention mechanism to inhibit the complex and changeable background features underwater, to improve the semantic feature extraction ability of the UPA-YOLO model for underwater targets, adding the MPDiou loss function to the improved algorithm model and completing the data validation of the detection model; finally, based on the TensorRT acceleration framework, the optimisation of the target detection Finally, based on the TensorRT acceleration framework, the target detection model is optimised, and the Jetson Nano edge device is used to complete the localisation deployment and realise the real-time target detection task of underwater sea treasures. …”
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Power Equipment Image Recognition Method Based on Feature Extraction and Deep Learning by Shuang Lin

“…We plan to introduce a lightweight convolutional structure combined with a graph neural network mechanism to strengthen global context modeling and device structural awareness. …”
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Dynamic atrous attention and dual branch context fusion for cross scale Building segmentation in high resolution remote sensing imagery by Yaohui Liu, Shuzhe Zhang, Xinkai Wang, Rui Zhai, Hu Jiang, Lingjia Kong

“…Among them, we introduced the Shift Operation module and the Self-Attention module, which adopt a dual-branch structure to respectively capture local spatial dependencies and global correlations, and perform weight coupling to achieve highly complementary contextual information fusion. …”
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Rice Disease Detection: TLI-YOLO Innovative Approach for Enhanced Detection and Mobile Compatibility by Zhuqi Li, Wangyu Wu, Bingcai Wei, Hao Li, Jingbo Zhan, Songtao Deng, Jian Wang

“…As a key global food reserve, rice disease detection technology plays an important role in promoting food production, protecting ecological balance and supporting sustainable agricultural development. …”
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FD-YOLO: A YOLO Network Optimized for Fall Detection by Hoseong Hwang, Donghyun Kim, Hochul Kim

“…First, a global attention module (GAM) based on the Convolutional Block Attention Module (CBAM) was employed to improve detection performance. …”
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Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism. by Tingyuan Zhang, Changsheng Zhang, Zhongyi Yang, Meng Wang, Fujie Zhang, Dekai Li, Sen Yang

“…The RSCD-Net architecture consists of 16 layers of SCR-Blocks, structured into four convolutional stages with 3, 4, 6, and 3 units, respectively. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. …”
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Bearing fault diagnosis based on efficient cross space multiscale CNN transformer parallelism by Qi Chen, Feng Zhang, Yin Wang, Qing Yu, Genfeng Lang, Lixiong Zeng

“…Subsequently, parallel branches are employed to extract spatio-temporal features: the Convolutional Neural Network (CNN) branch integrates a multiscale feature extraction module, a Reversed Residual Structure (RRS), and an Efficient Multiscale Attention (EMA) mechanism to enhance local and global feature extraction capabilities; the Transformer branch combines Bidirectional Gated Recurrent Units (BiGRU) and Transformer to capture both local temporal dynamics and long-term dependencies. …”
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Deep learning-based sex estimation of 3D hyoid bone models in a Croatian population using adapted PointNet++ network by Ivan Jerković, Željana Bašić, Ivana Kružić

“…The model, optimized for small datasets with 1D convolutional layers and global size features, was first applied in an unsupervised framework. …”
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Lightweight Road Environment Segmentation using Vector Quantization by J. Kwag, A. Yilmaz, C. Toth

“…Numerous works based on Fully Convolutional Networks (FCNs) and Transformer architectures have been proposed to leverage local and global contextual learning for efficient and accurate semantic segmentation. …”
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InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. …”
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Inpainting of damaged temple murals using edge- and line-guided diffusion patch GAN by G. Sumathi, M. Uma Devi

“…The WSFN uses the original image, a line drawing, and an edge map to capture mural details, which are then texturally inpainted in the SCN using gated convolution for enhanced results. Special attention is given to globally extending the receptive field for large-area inpainting. …”
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Infrared object detection for robot vision based on multiple focus diffusion and task interaction alignment by Jixu Zhang, Li Wang, Hung-Wei Li, Meng-Yen Hsieh, Shunxiang Zhang, Hua Wen, Meng Chen

“…The feature extraction module adopts a dual-stream fusion structure in the backbone network, which combines the local feature extraction of CNN with the global feature modeling of transformer. …”
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Machine Learning Prediction of Storm‐Time High‐Latitude Ionospheric Irregularities From GNSS‐Derived ROTI Maps by Lei Liu, Y. Jade Morton, Yunxiang Liu

“…Test results show that the convLSTM‐Lc algorithm can forecast irregularity structures more accurately than a convLSTM model that implements conventional loss functions. …”
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Applying SSVEP BCI on Dynamic Background by Junkai Li, Boxun Fu, Fu Li, Wenkai Gu, Youshuo Ji, Yang Li, Tiejun Liu, Guangming Shi

“…MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. …”
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TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction by Wenlong Wang, Peng Yu, Mengmeng Li, Xiaojing Zhong, Yuanrong He, Hua Su, Yunxuan Zhou

“…A bidirectional fusion module (BFM) is then designed to comprehensively integrate spatial details and global information, thereby enabling accurate identification of boundaries between adjacent buildings, and maintaining the structural integrity of buildings to avoid internal holes. …”
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A security data detection and management method in digital library network based on deep learning by Diyin Zhu, Yihang Wei, Jiali Cai, Jingwen Wang, Zhongshan Chen

“…The method combines the structures of temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU) to extract spatial and temporal features from digital library network security data. …”
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A hybrid learning approach for MRI-based detection of alzheimer’s disease stages using dual CNNs and ensemble classifier by Sepideh Zolfaghari, Atra Joudaki, Yashar Sarbaz

“…Abstract Alzheimer’s Disease (AD) and related dementias are significant global health issues characterized by progressive cognitive decline and memory loss. …”
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