Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble by Tossapon Boongoen, Natthakan Iam-On, James Mullaney

Get full text

Re-LSTM: A long short-term memory network text similarity algorithm based on weighted word embedding by Weidong Zhao, Xiaotong Liu, Jun Jing, Rongchang Xi

“…The word representation features and contextual relationships extracted by current text similarity computation methods are insufficient, and too many factors increase the computational complexity. …”
Get full text

Transformer Fault Diagnosis Based on Knowledge Distillation and Residual Convolutional Neural Networks by Haikun Shang, Yanlei Wei, Shen Zhang

“…Given the issues of large model parameters and high computational resource demands in transformer DGA diagnostics, this study proposes a lightweight convolutional neural network (CNN) model for improving gas ratio methods, combining Knowledge Distillation (KD) and recursive plots. …”
Get full text

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. …”
Get full text

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. …”
Get full text

SCR-Net: A novel lightweight aquatic biological detection network. by Tao Li, Yijin Gang, Sumin Li, Yizi Shang

“…Second, a cross-scale feature fusion pyramid (CFFP) structure is introduced, which significantly reduces the number of parameters and computational cost during feature fusion. …”
Get full text

Analysis and modeling of digital solutions in medical database management by Rakhimov Bakhtiyar, Rakhimova Feruza, Saidov Atabek, Saidova Zarina

“…This high accuracy is attributed to expanded Haar-like feature templates, efficient computation using integral images, and a comprehensive contour feature extraction process. …”
Get full text

Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic Microscopy by Mingjing Li, Junshuai Wang, Shu Fang, Le Yang, Xinyang Liu, Haijiao Yun, Xiaoli Wang, Qingyu Du, Ziqing Han

“…Furthermore, CCE-YOLOv7 reduced the number of parameters by 2 million and lowered computational complexity by 5.7 GFLOPs, offering an efficient and lightweight model suitable for real-time clinical applications. …”
Get full text

Lightweight remote sensing change detection with progressive multi scale difference aggregation by Yinghua Fu, Haifeng Peng, Tingting Zhao, Yize Li, Jiansheng Peng, Dawei Zhang

“…However, many previous neural network-based approaches require a large number of parameters and computations and high-performance hardware, which makes their practical application in remote sensing challenging. …”
Get full text

MSUD-YOLO: A Novel Multiscale Small Object Detection Model for UAV Aerial Images by Xiaofeng Zhao, Hui Zhang, Wenwen Zhang, Junyi Ma, Chenxiao Li, Yao Ding, Zhili Zhang

“…Furthermore, compared with multiple latest UAV object detection algorithms, our designed MSUD-YOLO offers higher detection accuracy and lower computational cost; e.g., mAP50 reaches 43.4%, but parameters are only 6.766 M.…”
Get full text

Early Sweet Potato Plant Detection Method Based on YOLOv8s (ESPPD-YOLO): A Model for Early Sweet Potato Plant Detection in a Complex Field Environment by Kang Xu, Wenbin Sun, Dongquan Chen, Yiren Qing, Jiejie Xing, Ranbing Yang

“…First, this method uses an efficient network model to enhance the information flow in the channel, obtain more effective global features in the high-level semantic structure, and reduce model parameters and computational complexity. …”
Get full text

A Heterogeneous Image Registration Model for an Apple Orchard by Dongfu Huang, Liqun Liu

“…Then, we used the Sinkhorn AutoDiff algorithm to iteratively optimize and solve the optimal transmission problem, achieving optimal matching between feature points. Finally, we carried out network pruning and compression operations to minimize parameters and computation cost while maintaining the model’s performance. …”
Get full text

A lightweight weed detection model for cotton fields based on an improved YOLOv8n by Jun Wang, Zhengyuan Qi, Yanlong Wang, Yanyang Liu

“…Finally, a lightweight detection head, LiteDetect, suitable for the BiFPN structure, is designed to streamline the model structure and reduce computational load. Experimental results show that compared to the original YOLOv8n model, YOLO-Weed Nano improves mAP by 1%, while reducing the number of parameters, computation, and weights by 63.8%, 42%, and 60.7%, respectively.…”
Get full text

Dual branch attention network for image super-resolution by Yiwei Hu, Yisu Ge, Mingming Qi, Shuhua Xu

“…Recently, the Transformer architecture has shown significant potential in image super-resolution due to its ability to perceive global features. Yet, the quadratic computational complexity of self-attention mechanisms in these Transformer-based methods leads to substantial computational and parameter overhead, limiting their practical application. …”
Get full text

Smoke Detection Transformer: An Improved Real-Time Detection Transformer Smoke Detection Model for Early Fire Warning by Baoshan Sun, Xin Cheng

“…Considering the limited computational resources of smoke detection devices, Enhanced Channel-wise Partial Convolution (ECPConv) is introduced to reduce the number of parameters and the amount of computation. …”
Get full text

Ship-DETR: A Transformer-Based Model for EfficientShip Detection in Complex Maritime Environments by Yi Wang, Xiang Li

“…First, we introduce the high-low frequency (HiLo) attention into the intra-scale feature interaction module to enhance the extraction of both high- and low-frequency features, reduce computational complexity, and improve detection performance. …”
Get full text

PPLIO: Plane-to-Plane LiDAR-Inertial Odometry With Multi-View Constraint in Real-Time by Hanyeol Lee, Jae Hyung Jung, Won Young Chung, Suyong Lee, Chan Gook Park

Get full text

Attention-Based Lightweight YOLOv8 Underwater Target Recognition Algorithm by Shun Cheng, Zhiqian Wang, Shaojin Liu, Yan Han, Pengtao Sun, Jianrong Li

“…Firstly, the SPDConv module is utilized in the backbone network to replace the standard convolutional module for feature extraction. This enhances computational efficiency and reduces redundant computations. …”
Get full text

A lightweight steel surface defect detection network based on YOLOv9 by Tianyi Zheng, Ling Yu, Yongbao Shi, Fanglin Niu

“…This approach improves the model’s feature extraction capability while reducing its parameter count. …”
Get full text

Lightweight Dual-Backbone Detection Transformer for Infrared Insulator Detection by Boyang Zhang, Yanpeng Zhang, Liming Sun

“…To extract target features from complex infrared backgrounds more accurately while reducing computational cost, we propose a multi-scale attention network (MSANet) as the adaptive backbone, which employs a multi-branch parallel structure and dynamically adjusts feature weights through a multi-scale gated attention mechanism. …”
Get full text