SOD-YOLO: A lightweight small object detection framework by Yunze Xiao, Nan Di

“…The DSD Module focuses on extracting both deep and shallow features from feature maps using fewer parameters to obtain richer feature representations. …”
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Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…Abstract Context Understanding protein-ligand interactions is fundamental to drug design, where optimizing docking parameter selection can potentially enhance computational efficiency and resource allocation in virtual screening. …”
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Multi-granularity representation learning with vision Mamba for infrared small target detection by Yongji Li, Luping Wang, Shichao Chen

“…Transformer with quadratic computational complexity struggles for local feature refinement. …”
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FUR-DETR: A Lightweight Detection Model for Fixed-Wing UAV Recovery by Yu Yao, Jun Wu, Yisheng Hao, Zhen Huang, Zixuan Yin, Jiajing Xu, Honglin Chen, Jiahua Pi

“…However, the existing RT-DETR algorithm is limited by single-path feature extraction, a simplified fusion mechanism, and high-frequency information loss, which makes it difficult to balance detection accuracy and computational efficiency. …”
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A Lightweight Dual-Branch Complex-Valued Neural Network for Automatic Modulation Classification of Communication Signals by Zhaojing Xu, Youchen Fan, Shengliang Fang, You Fu, Liu Yi

“…However, existing models face deployment challenges due to excessive parameters and computational complexity. To address these limitations, a lightweight dual-branch complex-valued neural network (LDCVNN) is proposed. …”
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A simple monocular depth estimation network for balancing complexity and accuracy by Xuanxuan Liu, Shuai Tang, Mengdie Feng, Xueqi Guo, Yanru Zhang, Yan Wang

“…Although research on monocular depth estimation is relatively mature, it commonly involves strategies that entail increasing both the computational complexity and the number of parameters to achieve superior performance. …”
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MEIS-YOLO: Improving YOLOv11 for efficient aerial object detection with lightweight design by Yicheng Liu, Jinsong Wu, Li Chen

“…Additionally, the cross bi-level routing attention module, which incorporates the cross-stage partial structure, optimizes the attention mechanism, further enhancing the model’s detection ability and computational efficiency. To further optimize multi-scale feature fusion, this paper introduces the asymptotic feature pyramid network. …”
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DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network by Peitao Cheng, Xuanjiao Lei, Haoran Chen, Xiumei Wang

“…However, efficient algorithms often come with a large number of parameters and high computational complexity. To meet the demand for high-performance object detection algorithms on mobile devices and embedded devices with limited computational resources, we propose a new lightweight object detection algorithm called DLE-YOLO. …”
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Expression Recognition Method Based on CBAM-DSC Network by SONG Wen bo, GAO Lu, MIAO Zhuang, LIN Ke zheng

“…The improved Inception module extracts different feature information through different branches while reducing the network parameters and improving the network operation efficiency. …”
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A Dual-Branches Multiscale Dynamic Partial Convolutional Attention Network for Remote Sensing Change Detection by Wenbin Tang, Shuli Cheng, Anyu Du

“…The MCA module integrates features from different levels, while the DPCATT module enables global interaction between dual-temporal features, thereby enhancing the global modeling capability of the dual-branch features, while reducing the computing resources. …”
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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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Lightweight Attention-Based CNN Architecture for CSI Feedback of RIS-Assisted MISO Systems by Anming Dong, Yupeng Xue, Sufang Li, Wendong Xu, Jiguo Yu

“…Specifically, the network employs 1D convolutional operations with unidirectional kernel sliding, which effectively reduces trainable parameters while maintaining robust feature-extraction capabilities. …”
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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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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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BurgsVO: Burgs-Associated Vertex Offset Encoding Scheme for Detecting Rotated Ships in SAR Images by Mingjin Zhang, Yaofei Li, Jie Guo, Yunsong Li, Xinbo Gao

“…Moreover, oriented bounding box-based detection methods often prioritize accuracy excessively, leading to increased parameters and computational costs, which in turn elevate computational load and model complexity. …”
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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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Multiview attention networks for fine-grained watershed categorization via knowledge distillation. by Huimin Gong, Cheng Zhang, Jinlin Teng, Chunqing Liu

“…However, first, the exploration of village-related watershed fine-grained classification problems, particularly the multi-view watershed fine-grained classification problem, has been hindered by dataset collection limitations; Second, village-related modeling networks typically employ convolutional modules for attentional modeling to extract salient features, yet they lack global attentional feature modeling capabilities; Lastly, the extensive number of parameters and significant computational demands render village-related watershed fine-grained classification networks infeasible for end-device deployment. …”
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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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