DiffMamba: semantic diffusion guided feature modeling network for semantic segmentation of remote sensing images by Zhen Wang, Nan Xu, Zhuhong You, Shanwen Zhang

“…MVTrans can observe the spatial location information of the object region from various perspectives to obtain refined global context details. SDAM utilizes the diffusion propagation process to fuse local and global information, alleviating the feature redundancy caused by semantic information differences. …”
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An Mcformer encoder integrating Mamba and Cgmlp for improved acoustic feature extraction by Nurmemet Yolwas, Yongchao Li, Lixu Sun, Jian Peng, Zhiwu Sun, Yajie Wei, Yineng Cai

“…To address this limitation, the Mcformer encoder is introduced, which incorporates the Mamba module in parallel with multi-head attention blocks to enhance the model’s global context processing capabilities. Additionally, a Convolutional Gated Multilayer Perceptron (Cgmlp) structure is employed to improve the extraction of local features through deep convolutional layers. …”
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Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents by Yipeng Zhou, Jiaxin Fan, Qingchuan Zhang, Lin Zhu, Xingchen Sun

“…By constructing a document–sentence–entity heterogeneous graph and employing graph convolutional networks (GCNs), the model effectively captures global semantic associations and interactions between cross-sentence triggers and arguments. …”
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Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning by Teemu Koivisto, Marcin Mińkowski, Lasse Laurson

“…We first train a convolutional neural network using data from molecular dynamics simulations to learn the mapping from the sample-specific initial atomic structure to features of the stress–strain curve. …”
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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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Rain removal method for single image of dual-branch joint network based on sparse transformer by Fangfang Qin, Zongpu Jia, Xiaoyan Pang, Shan Zhao

“…Indeed, RSTB preserves the most valuable self-attention values for the aggregation of features, facilitating high-quality image reconstruction from a global perspective. Finally, the parallel dual-branch joint module, composed of RSTB and UEDB branches, effectively captures the local context and global structure, culminating in a clear background image. …”
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DASNet a dual branch multi level attention sheep counting network by Yini Chen, Ronghua Gao, Qifeng Li, Hongtao Zhao, Rong Wang, Luyu Ding, Xuwen Li

“…DASNet is built on a modified VGG–19 architecture, where a dual-branch structure is employed to integrate both shallow and deep features. …”
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MFMamba: A Mamba-Based Multi-Modal Fusion Network for Semantic Segmentation of Remote Sensing Images by Yan Wang, Li Cao, He Deng

“…Specifically, the network employs a dual-branch encoding structure, consisting of a CNN-based main encoder for extracting local features from high-resolution remote sensing images (HRRSIs) and of a Mamba-based auxiliary encoder for capturing global features on its corresponding digital surface model (DSM). …”
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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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A Spoofing Speech Detection Method Combining Multi-Scale Features and Cross-Layer Information by Hongyan Yuan, Linjuan Zhang, Baoning Niu, Xianrong Zheng

“…Spoofing speech detection, which is a pressing issue in the age of generative AI, requires both global information and local features of speech. The multi-layer transformer structure in pre-trained speech models can effectively capture temporal information and global context in speech, but there is still room for improvement in handling local features. …”
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Hierarchical Fusion of Infrared and Visible Images Based on Channel Attention Mechanism and Generative Adversarial Networks by Jie Wu, Shuai Yang, Xiaoming Wang, Yu Pei, Shuai Wang, Congcong Song

“…The results show that the proposed algorithm retains the global structure features of multilayer images and has obvious advantages in fusion performance, model generalization and computational efficiency.…”
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DFPF-Net: Dynamically Focused Progressive Fusion Network for Remote Sensing Change Detection by Chengming Wang, Peng Duan, Jinjiang Li

“…To address these challenges, we propose the dynamically focused progressive fusion network (DFPF-Net) to simultaneously tackle global and local noise influences. On one hand, we utilize a pyramid vision transformer (PVT) as a weight-shared siamese network to implement change detection, efficiently fusing multilevel features extracted from the pyramid structure through a residual based progressive enhanced fusion module (PEFM). …”
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Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines by Anxin Zhao, Qiuhong Zheng, Liang Li

“…For the discriminator network, a global-local discriminator structure is designed to optimize overall illumination while adaptively enhancing shadow and highlight regions. …”
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A Medical Image Semantics Segmentation Method Based on Image Pre-processing and Image Transformer by Li Zhaopeng

“…Existing models in this field pay more attention to editing U-Net’s structure and convolutional layers. In this paper, an image pre-processing technique to enrich the potential of images and an image transformer involved in network TrUNet are proposed to tackle these issues. …”
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MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models by Feng Wang, Jinming Chu, Liyan Shen, Shan Chang

“…Finally, MESM uses Graph Convolutional Network (GCN) and SubgraphGCN to extract global and local features from the perspective of the overall graph and subgraphs. …”
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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

“…Abstract This study presents an image‐based convolutional long short‐term memory (convLSTM) machine learning algorithm to predict storm‐time ionospheric irregularities. …”
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AutoLDT: a lightweight spatio-temporal decoupling transformer framework with AutoML method for time series classification by Peng Wang, Ke Wang, Yafei Song, Xiaodan Wang

“…Notably, we adopt the Covariance Matrix Adaptation Evolution Strategy and global adaptive pruning technique to realize automated network structure design, which further improves the model training efficiency and automation, and avoids the uncertainty problem of network design. …”
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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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Fusion of Recurrence Plots and Gramian Angular Fields with Bayesian Optimization for Enhanced Time-Series Classification by Maria Mariani, Prince Appiah, Osei Tweneboah

“…Time-series classification remains a critical task across various domains, demanding models that effectively capture both local recurrence structures and global temporal dependencies. We introduce a novel framework that transforms time series into image representations by fusing recurrence plots (RPs) with both Gramian Angular Summation Fields (GASFs) and Gramian Angular Difference Fields (GADFs). …”
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MFPI-Net: A Multi-Scale Feature Perception and Interaction Network for Semantic Segmentation of Urban Remote Sensing Images by Xiaofei Song, Mingju Chen, Jie Rao, Yangming Luo, Zhihao Lin, Xingyue Zhang, Senyuan Li, Xiao Hu

“…The Swin Transformer efficiently extracts multi-level global semantic features through its hierarchical structure and window attention mechanism. …”
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