FaultVitNet: A Vision Transformer Assisted Network for 3D Fault Segmentation by Chao Li, Sergey Fomel, Yangkang Chen, Robin Dommisse, Alexandros Savvaidis

“…The proposed vision‐transformer‐assisted network (FaultVitNet) exploits a hybrid attention mechanism for feature extraction, enabling the network to capture the global distribution information of faults. …”
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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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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

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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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YOLO-LPSS: A Lightweight and Precise Detection Model for Small Sea Ships by Liran Shen, Tianchun Gao, Qingbo Yin

“…The characteristics of YOLO-LPSS are as follows: (1) Strengthening the backbone’s ability to extract and emphasize features relevant to small ship objects, particularly in semantic-rich layers. (2) A sophisticated, learnable method for up-sampling processes is employed, taking into account both deep image information and semantic information. (3) Introducing a post-processing mechanism in the final output of the resampling process to restore the missing local region features in the high-resolution feature map and capture the global-dependence features. …”
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DOA Estimation Based on CNNs Embedded With Mamba by Zhang Ziyan, Yi Shichao, Wang Chengyi

“…However, feature processing of the original signal inevitably loses part of the information, and using the original signal directly makes the number of parameters and computation huge. …”
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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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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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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…Second, we design a multi-receptive attention and interaction mechanism to perceive global and local correlations of the images in every transformer block for effective feature learning for small-sized networks. Extensive experiments show that the proposed lightweight IMViT-B outperforms DeiTIII, this paper IMViT-B(300 epochs) achieves a top accuracy of <inline-formula> <tex-math notation="LaTeX">$82.8~\%$ </tex-math></inline-formula> on ImageNet-1K with only 26M parameters, surpasses the DeiTIII-S(800 epochs) +1.4%, with a similar number of parameters and computation cost. …”
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A lightweight detection algorithm of PCB surface defects based on YOLO. by Shiwei Yu, Feng Pan, Xiaoqiang Zhang, Linhua Zhou, Liang Zhang, Jikui Wang

“…Finally, the PANet network structure is replaced with the bidirectional feature pyramid network (BIFPN) structure to enhance the fusion of multi-scale features in the network. …”
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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. …”
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Multiple Chaos Synchronization System for Power Quality Classification in a Power System by Cong-Hui Huang, Chia-Hung Lin

“…The proposed method can adapt itself without the need for adjustment of parameters or iterative computation. For a sample power system, the test results showed accurate discrimination, good robustness, and faster processing time for the detection of PQ disturbances.…”
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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. …”
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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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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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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. …”
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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.…”
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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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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. …”
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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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