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

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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. …”
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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. …”
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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. …”
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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. …”
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Research on PCB defect detection algorithm based on LPCB-YOLO by Haiyan Zhang, Haiyan Zhang, Yazhou Li, Yazhou Li, Dipu Md Sharid Kayes, Dipu Md Sharid Kayes, Zhaoyu Song, Zhaoyu Song, Yuanyuan Wang, Yuanyuan Wang

“…The goal was to ensure detection accuracy and comprehensiveness while significantly reducing model parameters and improving computational speed.MethodFirst, the feature extraction networks consist of multiple CSPELAN modules for feature extraction of small target defects on PCBs. …”
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Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS by Mengyuan Zhao, Beibei Cui, Yuehao Yu, Xiaoyi Zhang, Jiaxin Xu, Fengzheng Shi, Liang Zhao

“…This module performs convolution in stages on the feature map, generating more feature maps with fewer parameters and computational resources, thereby improving the model’s feature extraction capability while reducing parameter count and computational cost. …”
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MAMNet: Lightweight Multi-Attention Collaborative Network for Fine-Grained Cropland Extraction from Gaofen-2 Remote Sensing Imagery by Jiayong Wu, Xue Ding, Jinliang Wang, Jiya Pan

“…To address the issues of high computational complexity and boundary feature loss encountered when extracting farmland information from high-resolution remote sensing images, this study proposes an innovative CNN–Transformer hybrid network, MAMNet. …”
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GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8 by Qiang Hu, Yunhua Zhang

“…The number of parameters and computational load of the improved model have been reduced by 45.7% and 45.1%, respectively, compared to the baseline model, while the mAP has increased by 1.3%. …”
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GES-YOLO: A Light-Weight and Efficient Method for Conveyor Belt Deviation Detection in Mining Environments by Hongwei Wang, Ziming Kou, Yandong Wang

“…To address issues such as the high computational complexity, large number of parameters, long inference time, and difficulty in feature extraction of existing conveyor belt deviation detection models, we propose a GES-YOLO algorithm for detecting deviation in mining belt conveyors, based on an improved YOLOv8s model. …”
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FQDNet: A Fusion-Enhanced Quad-Head Network for RGB-Infrared Object Detection by Fangzhou Meng, Aoping Hong, Hongying Tang, Guanjun Tong

“…FQDNet was evaluated on three public RGB-IR datasets—M3FD, VEDAI, and LLVIP—achieving mAP@[0.5:0.95] gains of 4.4%, 3.5%, and 3.1% over the baseline, with only a 0.4 M increase in parameters and 5.5 GFLOPs overhead. Compared to state-of-the-art RGB-IR object detection algorithms, our method strikes a better balance between detection accuracy and computational efficiency while exhibiting strong robustness across diverse detection scenarios.…”
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The TDGL Module: A Fast Multi-Scale Vision Sensor Based on a Transformation Dilated Grouped Layer by Leilei Xie, Fenghua Zhu, Zhixue Wang

“…These improvements enable the network to distinguish features at different scales effectively while optimizing spatial information processing and reducing computational costs. …”
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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. …”
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YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection by Xiang Gan, Shukun Cao, Jin Wang, Yu Wang, Xu Hou

“…Based on the original YOLOv8n, the algorithm replaced the Conv module with the DSConv module in the backbone network, which reduced the backbone network parameters and computational load and improved the detection accuracy at the same time. …”
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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. …”
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A lightweight transformer with linear self‐attention for defect recognition by Yuwen Zhai, Xinyu Li, Liang Gao, Yiping Gao

“…LSA‐Former proposes a novel self‐attention with linear computational complexity, enabling it to capture local and global semantic features with fewer parameters. …”
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A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation by Qingxu Meng, Weijiang Wang, Hang Qi, Hua Dang, Minli Jia, Xiaohua Wang

“…Although the combination of CNNs and Transformers balances the ability to capture local detailed features and global context information, it inevitably increases the model’s parameters and computational cost, which restricts its equal deployment in real medical scenarios. …”
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A deep learning-based algorithm for the detection of personal protective equipment. by Bo Tong, Guan Li, Xiangli Bu, Yang Wang, Xingchen Yu

“…Additionally, structured pruning techniques were applied to the model at varying levels, further reducing computational and parameter loads. Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”
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Study on lightweight strategies for L-YOLO algorithm in road object detection by Ji Hong, Kuntao Ye, Shubin Qiu

“…This modification improves the capture of features and contextual information for small vehicle targets without significantly increasing computational demands. …”
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Real-Time Transformer Detection of Underwater Objects Based on Lightweight Gated Convolutional Network by Yuhui LI, Huixia CUI, Yaomin LI, Senping JIA

“…To address the challenges in underwater object detection algorithms, including difficult image feature processing, redundant model architectures, and excessive parameter numbers, this paper proposed a real-time Transformer detection method for underwater objects based on a lightweight gated convolutional network. …”
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