A crop model based on dual attention mechanism for large area adaptive yield prediction by Wei Xiang, Long Long, Zichen Liu, Feng Dai, Yucheng Zhang, Hu Li, Lin Cheng

“…Although existing models have improved accuracy by increasing model complexity and coupling different deep learning models, their generalization performance is poor due to significant spatial differences in crop growth environments, making it difficult to explore common features of crop environments in different regions.To address this issue, this paper comprehensively considers crop growth cycles and environmental factors such as soil and weather, presenting a large-scale crop yield prediction model based on an attention mechanism.The model consists of two modules: time attention module and feature attention module. …”
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Hash-Guided Adaptive Matching and Progressive Multi-Scale Aggregation for Reference-Based Image Super-Resolution by Lin Wang, Jiaqi Zhang, Huan Kang, Haonan Su, Minghua Zhao

“…Firstly, to address the issue of feature matching, this chapter proposes a hash adaptive matching module. …”
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Construction of a traffic flow prediction model based on neural ordinary differential equations and Spatiotemporal adaptive networks by Li Ma, Yunshun Wang, Xiaoshi Lv, Lijun Guo

“…In the short-term spatiotemporal branch, a recurrent neural ordinary differential equation (ODE) module is devised. This module is capable of continuously and dynamically adjusting short-term spatiotemporal features, better capturing and exploring potential short-term spatiotemporal characteristics. …”
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SCA-CVENet: A Spatial-Channel-Attention Collaborative Cost Volume Enhancement Network for High-Quality Depth Reconstruction by Mao Tian, Xudong Zhao, Xiong Lv

“…Second, the SAW generation subnetwork is adopted, which incorporates the group-wise correlation (GWC)-based cost volume adaptive fusion module and 3D hourglass module to produce an accurate SAW. …”
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Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 by ZHU Yongjun, CAI Guangqi, HAN Jin, MIAO Yanzi, MA Xiaoping, JIAO Wenhua

“…The improved YOLOv11 model introduced a Robust Feature Downsampling (RFD) module to replace the stride convolution downsampling module, effectively preserving the feature information of small objects. …”
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YOLO-CSMD: Integrating Improved Convolutional Techniques for Manhole Cover Defect Detection by Zhiwang Xu, Haowei Luo, Huijie Zhu, Wanfa Sun, Shengying Yang

“…Recent detection algorithms for manhole cover defects exhibit limited detection capabilities, which frequently encounter the issue of missed detection. To address this issue, this paper proposes an improved detection algorithm based on YOLOv8, called YOLO-CSMD. …”
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A text classification model for dynamic fusion of global and local features by ZHENG Wenjun, ZHANG Shunxiang

“…Existing text classification models insufficiently utilize global and local information in texts, leading to subpar classification performance. In response to this issue, a text classification model called global and local features dynamic fusion (GLFDF) is proposed. …”
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Design and Implementation of Level Shifted SPWM for 3-Phase 3-Level Inverter by Ahmad Qurthubi Salman Al-Khansa, I Made Wiwit Kastawan, Nanang Mulyono

“…This research aims to produce a module that can issue a signal to trigger 3 levels of 3-phase inverter using the level-shifted SPWM switching method. …”
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Les connaissances liées à l’apprentissage de la lecture dans un dictionnaire électronique by Chrysta Pelissier

“…On the other hand, we illustrate our comments using the Lexical Resource Module (LRM hereafter), for how a child learns to read at the outset. …”
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A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies by Jin Lu, Zhongji Cao, Jin Wang, Zhao Wang, Jia Zhao, Minjie Zhang

“…More specifically, the network PGSS-YOLOv11s is composed of an original backbone of the YOLOv11s-seg, a spatial feature aggregation module (SFAM), an adaptive feature fusion module (AFFM), and a detail-enhanced convolutional shared detection head (DE-SCSH). …”
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Identification of sweetpotato virus disease-infected leaves from field images using deep learning by Ziyu Ding, Fanguo Zeng, Haifeng Li, Jianyu Zheng, Junzhi Chen, Biao Chen, Wenshan Zhong, Xuantian Li, Zhangying Wang, Lifei Huang, Xuejun Yue, Xuejun Yue

“…This study employs a deep learning-based semantic segmentation network, DeepLabV3+, integrated with an Attention Pyramid Fusion (APF) module. The APF module combines a channel attention mechanism with multi-scale feature fusion to enhance the model’s performance in disease pixel segmentation. …”
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YOLOv11-RDTNet: A Lightweight Model for Citrus Pest and Disease Identification Based on an Improved YOLOv11n by Qiufang Dai, Shiyao Liang, Zhen Li, Shilei Lyu, Xiuyun Xue, Shuran Song, Ying Huang, Shaoyu Zhang, Jiaheng Fu

“…Second, the Dynamic Group Shuffle Transformer (DGST) module replaces the original C3k2 module, reducing the model’s parameter count and computational demand, further enhancing efficiency and performance. …”
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MSDNet: A Multi-Scale Feature Representation Network Model for Tunnel Bolt Detection by Xiufeng Wu, Xueli Li, Guangxing Cao, Chao Guo, Wei Chang, Zhao Wang, Shuliang Liu

“…Additionally, we develop a feature enhancement module (FEM) to capture more detailed features in small or localized areas during the search stage. …”
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A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images by Dawen Yu, Shunping Ji

“…To achieve accurate instance mask segmentation, we introduce a shape guidance module (SGM) to emphasize the object boundary and shape information. …”
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Underwater image enhancement via multiscale disentanglement strategy by Jiaquan Yan, Hao Hu, Yijian Wang, Muhammad Wasim Nawaz, Naveed Ur Rehman Junejo, Ente Guo, Huibin Feng

“…To address the above issues, we propose a Multiscale Disentanglement Network (MD-Net) for Underwater Image Enhancement (UIE), which mainly consists of scene radiance disentanglement (SRD) and transmission map disentanglement (TMD) modules. …”
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MSANet: LiDAR-Camera Online Calibration with Multi-Scale Fusion and Attention Mechanisms by Fengguang Xiong, Zhiqiang Zhang, Yu Kong, Chaofan Shen, Mingyue Hu, Liqun Kuang, Xie Han

“…By fusing multi-scale features, we obtain feature representations that contain a lot of detail and rich semantic information. The attention module is used to carry out feature correlation among different modes to complete feature matching. …”
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ICCAT Inspections in Turkey and Turkey’s National Legislation Compliance With the ICCAT Recommendations by Raziye Tanrıverdi

“…The necessary information and incentives should be provided for the fishing vessels of less than 12 m in length, which had obtained swordfish fishery permits, to use fuel without special consumption tax, in order to monitor them electronically via Vessel Tracking Module. In order to control the quota in swordfish fishery, it should be obligatory to use a paper logbook for fishing vessels less than 12 meters in length. …”
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Enhancing Road Scene Segmentation With an Optimized DeepLabV3+ by Zhe Ren, Libao Wang, Tianming Song, Yihang Li, Jian Zhang, Fengfeng Zhao

“…Second, the Atrous Spatial Pyramid Pooling (ASPP) module is optimized by introducing depthwise separable convolutions and a hierarchical feature fusion strategy, reducing computational complexity and mitigating the grid effect, a limitation in many current models. …”
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YOLO-Wheat: A More Accurate Real-Time Detection Algorithm for Wheat Pests by Yongkang Liu, Qinghao Wang, Qi Zheng, Yong Liu

“…We provide an enhanced algorithm, YOLO-Wheat, based on YOLOv8, to solve the aforementioned issues. The proposed YOLO-Wheat, an extension of YOLOv8, integrates SimAM into the C2f module to enhance feature extraction capabilities. …”
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Blind Cartography for Side Channel Attacks: Cross-Correlation Cartography by Laurent Sauvage, Sylvain Guilley, Florent Flament, Jean-Luc Danger, Yves Mathieu

“…In this paper, we present a localisation method based on cross-correlation, which issues a list of areas of interest within the attacked device. …”
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