GIVTED-Net: GhostNet-Mobile Involution ViT Encoder-Decoder Network for Lightweight Medical Image Segmentation by Resha Dwika Hefni Al-Fahsi, Ahmad Naghim Fauzaini Prawirosoenoto, Hanung Adi Nugroho, Igi Ardiyanto

“…Nevertheless, conventional CNN layers, such as convolution and pooling, demonstrate a spatial inductive bias that constrains their ability to instantly capture global context information. …”
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Enhancing Real-Time Aerial Image Object Detection with High-Frequency Feature Learning and Context-Aware Fusion by Xin Ge, Liping Qi, Qingsen Yan, Jinqiu Sun, Yu Zhu, Yanning Zhang

“…Specifically, our approach integrates three innovative modules: (1) the VHeat C2f module, which employs a frequency-domain heat conduction mechanism to fine-tune feature representations and significantly enhance high-frequency detail extraction; (2) the Multi-scale Feature Aggregation and Distribution Module (MFADM), which utilizes large convolution kernels of different sizes to robustly capture effective high-frequency features; and (3) the Context Attention Guided Fusion Module (CAGFM), which ensures precise and effective fusion of high-frequency contextual information across scales, substantially improving the detection accuracy of small objects. …”
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POI recommendation by deep neural matrix factorization integrated attention-aware meta-paths by Xiaoyan Li, Shenghua Xu, Hengxu Jin, Zhuolu Wang, Yu Ma, Xuan He

“…This outcome is achieved using generalized matrix factorization and convolution-constrained multi-head self-attention mechanism deep neural networks. …”
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CenterNet-Elite: A Small Object Detection Model for Driving Scenario by Lingling Wang, Xiang Li, Xiaoyan Chen, Bin Zhou

“…This module obtains receptive fields of different sizes through max-pooling layers of diverse sizes, adapting to changes in object scale on the feature maps. …”
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DOA Estimation by Feature Extraction Based on Parallel Deep Neural Networks and MRMR Feature Selection Algorithm by Ashwaq Neaman Hassan Al-Tameemi, Mahmood Mohassel Feghhi, Behzad Mozaffari Tazehkand

“…In parallel, the proposed model extracts spatial and temporal features using a convolution neural network (CNN) and long short-term memory (LSTM). …”
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Efficient detection of highway pavement cracks using computer vision-based MSFNet model by Zhengfa Jiang, Danlan Li, Ting Zhao, Mingxing Gao

“…It uses blueprint separable convolution(BSConv) and MobileViT block to build a lightweight local and global crack feature extraction network, and incorporates a Self-Focus module to avoid feature information loss; Furthermore, to automatically correct the restoration of feature maps at different levels, we design a multilevel correction decoder(MCD) based on the coordinate-aware fusion(CAF) module; Then, combine it with a linear feature refining head to make the network's receptive field more consistent with the crack distribution in real scenarios. …”
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A Self-Supervised Monocular Depth Estimation Framework Based on Detail Recovery and Feature Fusion by Shun Li, Chongzheng Huang, Xiangzhe Li, Zhengyou Liang

“…MDAFM captures feature information from different perspectives and levels to address the problem of critical information loss, while HPCB effectively captures and fuses local and global information through parallel processing. …”
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Refined Anchor-Free Model With Feature Enhancement Mechanism for Ship Detection in Infrared Images by Yunlong Gao, Chuan Wu, Ming Ren, Yang Feng

“…Secondly, a multiattention fusion module based on convolution kernels with different sizes is embedded into the backbone to distinguish the spatial salience of the targets from the complex surroundings. …”
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Real-Time Wave Energy Converter Control Using Instantaneous Frequency by Inyong Kim, Ted K. A. Brekken, Solomon Yim, Brian Johnson, Yue Cao, Pranav Chandran

“…Traditional hydrodynamic modeling techniques may require computationally intensive convolution calculations, making real-time control implementation challenging. …”
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Polarization of road target detection under complex weather conditions by Feng Huang, Junlong Zheng, Xiancai Liu, Ying Shen, Jinsheng Chen

“…This module integrates channel-wise global self-attention and small kernel convolution to adaptively adjust the polarization enhancement method using dynamically extracted global and local polarization feature information. …”
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GrotUNet: a novel leaf segmentation method by Hongfei Deng, Hongfei Deng, Bin Wen, Bin Wen, Cheng Gu, Yingjie Fan

“…Unlike UNet++ dense connectivity, jump connection reconstruction only uses 1×1  convolution for feature fusion of feature maps from different network hierarchies to enrich the semantic information at each location in the space. …”
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Physiological signal-based mental stress detection using hybrid deep learning models by Nandini Modi, Yogesh Kumar, Kapil Mehta, Neelam Chaplot

“…In this study, a hybrid deep learning model combining Convolution neural network (CNN) with Multilayer perceptron (MLP) is proposed to classify mental stress levels based on physiological signal data. …”
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Cross-Domain Person Re-Identification Based on Multi-Branch Pose-Guided Occlusion Generation by Pengnan Liu, Yanchen Wang, Yunlong Li, Deqiang Cheng, Feixiang Xu

“…Finally, a dynamic convolution kernel is constructed to calculate the similarity between images. …”
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MMI Couplers and the Talbot Effect, Symmetries and Golden Ratio by Gazi Mahamud Hasan, Mehedi Hasan, Karin Hinzer, Trevor Hall

“…It may be modeled by a shift-invariant linear system, fully characterized by the convolution of its impulse response. Self-imaging at integer and fractional Talbot distances of point sources on a regular grid in free space may then be represented by a transmission matrix that is circulant, symmetric, and persymmetric. …”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

“…An attention-guided network (AGNet) is a network that utilizes attention to guide information across different scales. Its feature extraction module employs a dual-branch information residual unit (DIRU) as a substitute for the conventional convolution block, which combines the feature extraction capabilities of global pooling and max pooling, reducing the number of parameters while also achieving a certain improvement in detection results. …”
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Neural Networks Detection of Low-Amplitude Components on ECG Using Modified Wavelet Transform by A. V. Mnevets, N. G. Ivanushkina

“… This study is devoted to identification of low amplitude components from ECG signals by different time-frequency analysis methods when main power spectrum falls on high-amplitude components. …”
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SECrackSeg: A High-Accuracy Crack Segmentation Network Based on Proposed UNet with SAM2 S-Adapter and Edge-Aware Attention by Xiyin Chen, Yonghua Shi, Junjie Pang

“…This function also applies Multi-Granularity Supervision by optimizing segmentation outputs at three different resolution levels, ensuring better feature consistency and improved model robustness across varying image scales. …”
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ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting by Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao

“…Secondly, some models ignore the different impacts of nodes in the global road network on traffic. …”
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Harnessing Syntax GCN and Multi-View Interaction for Conversational Aspect-Based Quadruple Sentiment Analysis by Chunling Wu, Houwei Kang

“…Therefore, the analysis needs to consider both the syntactic structure of individual utterances and the interactions between different views. However, previous studies often lack modeling of dialogue features and overlook the interdependence between utterances. …”
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Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images by José Almeida, Jan Kubicek, Marek Penhaker, Martin Cerny, Martin Augustynek, Alice Varysova, Avinash Bansal, Juraj Timkovic

“…The segmentation pipeline is combined with different pre-trained Convolution Neural Network architectures to evaluate its automatic classification capabilities. …”
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