Meta-Learning-Based Lightweight Method for Food Calorie Estimation by Jinlin Ma, Yuetong Wan, Ziping Ma

“…Then, to achieve efficient calorie estimation with lower computational complexity, the calorie estimation module employs query-based inference to achieve optimal feature expression. …”
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GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm by Xiangqiang Kong, Guangmin Liu, Yanchen Gao

“…First, a new lightweight module, C2f-GE, is designed to replace the C2f module of the backbone network, which effectively reduces the computational parameters, and at the same time increases the number of channels of the feature map to enhance the feature extraction capability of the model. …”
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Noise-agnostic quantum error mitigation with data augmented neural models by Manwen Liao, Yan Zhu, Giulio Chiribella, Yuxiang Yang

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DEMNet: A Small Object Detection Method for Tea Leaf Blight in Slightly Blurry UAV Remote Sensing Images by Yating Gu, Yuxin Jing, Hao-Dong Li, Juntao Shi, Haifeng Lin

“…DEMNet introduces a dynamic convolution mechanism into the HGNetV2 backbone to form DynamicHGNetV2, enabling adaptive convolutional weight generation and improving feature extraction for blurry objects. An efficient EMAFPN neck structure further facilitates deep–shallow feature interaction while reducing the computational cost. …”
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Three-Stage Channel Split Dense Fusion Network for Single Image Deraining by ZHANG Shuting, WANG Changyue, WANG Changzhong, LENG Qiangkui

“…CSB uses channel split operation to split the rainy image into multiple channels, and applies different rain streaks removal methods according to different levels of features to reduce redundant features and network parameters, and improve the performance ability and computational efficiency of the model. …”
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Research on Vehicle Target Detection Method Based on Improved YOLOv8 by Mengchen Zhang, Zhenyou Zhang

“…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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Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics by Chaoquan Mo, Ke Huang, Houxin Ji, Wenhan Li, Kaibo Xu

“…This method combines the local feature extraction capability of CNNs with the global dependency capturing ability of ViTs, while maintaining computational efficiency. …”
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A Lightweight Network with Domain Adaptation for Motor Imagery Recognition by Xinmin Ding, Zenghui Zhang, Kun Wang, Xiaolin Xiao, Minpeng Xu

“…This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model’s parameters and improving the real-time performance and computational efficiency. …”
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Fast Decomposition Algorithm Based on Two-Dimensional Wavelet Transform for Image Processing of Graphic Design by Feifei Jiang, Wenting Yao

“…In this paper, we propose a fast decomposition algorithm image processing method based on a new transform of the wavelet transform, which mainly addresses the problems of large computation of feature points and long-time consumption of traditional image processing algorithms. …”
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NGSTGAN: N-Gram Swin Transformer and Multi-Attention U-Net Discriminator for Efficient Multi-Spectral Remote Sensing Image Super-Resolution by Chao Zhan, Chunyang Wang, Bibo Lu, Wei Yang, Xian Zhang, Gaige Wang

“…Recent advancements in convolutional neural networks (CNNs) and Transformers have significantly improved RSISR performance due to their capabilities in local feature extraction and global modeling. However, several limitations remain, including the underutilization of multi-scale features in RSIs, the limited receptive field of Swin Transformer’s window self-attention (WSA), and the computational complexity of existing methods. …”
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LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images by Zhixing Ma, Peidong Luo, Xiaole Shen

“…This upgrade strengthens the network’s ability to capture fine details and complex patterns, improving multi-scale feature extraction without a significant increase in parameters. …”
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Dense Sandstone Material Decomposition Based on Improved Convolutional Neural Network by Ran ZHANG, Huihua KONG, Jiaxin LI, Yijiao SONG

“…The proposed method uses the structure of the U-Net network and Resnet-152 as the backbone network to extract multi-scale features. Parallel asymmetric convolution is used to complete the large kernel convolution, which reduces the number of parameters and computation of the network. …”
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BinaryViT: Binary Vision Transformer for Hyperspectral Image Classification by Xiang Hu, Taolin Liu, Zhe Guo, Yuxiang Tang, Yuanxi Peng, Tong Zhou

“…As a key technology for lightweight deep models, binary quantization achieves significant parameter compression and computational acceleration by binarizing activations and weights. …”
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A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection by Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang, Ying Huang

“…The SggNet adopts a classical encoder-decoder structure with MobileNet-V2 as the backbone, ensuring optimal parameter utilization. Furthermore, we design an Efficient Global Perception Module (EGPM) to capture global feature relationships and semantic cues through limited computational costs, enhancing the model’s ability to perceive salient objects in complex scenarios, and a Semantic-Guided Edge Awareness Module (SEAM) that leverages the semantic consistency of deep features to suppress background noise in shallow features, accurately predict object boundaries, and preserve the detailed shapes of salient objects. …”
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A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture by Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin

“…Automated harvesting of “Sunshine Rose” grapes requires accurate detection and classification of grape clusters under challenging orchard conditions, such as occlusion and variable lighting, while ensuring that the model can be deployed on resource- and computation-constrained edge devices. This study addresses these challenges by proposing a lightweight YOLOv8-based model, incorporating DualConv and the novel C2f-GND module to enhance feature extraction and reduce computational complexity. …”
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YOLO-PEL: The Efficient and Lightweight Vehicle Detection Method Based on YOLO Algorithm by Zhi Wang, Kaiyu Zhang, Fei Wu, Hongxiang Lv

“…YOLOv8-PEL shows outstanding performance in detection accuracy, computational efficiency, and generalization capability, making it suitable for real-time and resource-constrained applications. …”
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TCSR: Lightweight Transformer and CNN Interaction Network for Image Super-Resolution by Danlin Cai, Wenwen Tan, Feiyang Chen, Xinchi Lou, Jianbin Xiahou, Daxin Zhu, Detian Huang

“…Recent Transformer has attracted increasing attention in lightweight SR methods owing to its remarkable global feature extraction capacity. However, the huge computational cost makes it challenging for lightweight SR methods to efficiently utilize Transformer to exploit global contextual information from shallow to intermediate layers. …”
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PFW-YOLO Lightweight Helmet Detection Algorithm by Yue Hong, Hao Wang, Shuo Guo

“…Firstly, a multi-scale feature fusion module is designed to reconstruct the Bottleneck structure in C2f, which finally forms the C2f-PMSFF module to enhance the feature expression ability of the model and optimize the computational efficiency. …”
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Research on real-time monitoring method of mine personnel protective equipment with improved YOLOv8 by Lei ZHANG, Zhipeng SUN, Hongjing TAO, Shangkai HAO, Qianru YAN, Xiwei LI

“…Detection real-time improved to 65 f·s−1, An increase of 8.3%, In addition, the number of parameters, floating point computation and model volume are 2 M, 6.6 G and 4.4 MB respectively. …”
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LMD-YOLO: A lightweight algorithm for multi-defect detection of power distribution network insulators based on an improved YOLOv8. by Weiyu Han, Zixuan Cai, Xin Li, Anan Ding, Yuelin Zou, Tianjun Wang

“…The SimAM attention mechanism is integrated to suppress irrelevant features and enhance feature extraction capabilities without adding extra parameters. …”
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