LocaLock: Enhancing Multi-Object Tracking in Satellite Videos via Local Feature Matching by Lingyu Kong, Zhiyuan Yan, Hanru Shi, Ting Zhang, Lei Wang

“…Additionally, the local computation within the LCV module ensures low computational complexity and memory usage. …”
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EHAFF-NET: Enhanced Hybrid Attention and Feature Fusion for Pedestrian ReID by Jun Yang, Yan Wang, Haizhen Xie, Jiayue Chen, Shulong Sun, Xiaolan Zhang

“…The spatial attention mechanisms aggregate features using global average and max pooling to enhance spatial representation. To tackle issues like perspective differences, lighting changes, and occlusions, we incorporate the Multi-Branch Feature Integration module. …”
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CCMCS-Net: Integrating Color Correction and Multicolor-Space Stretching for Improving Underwater Image Quality by Jianjun Chen, Yujie Yang, Jingjing Song, Xibei Yang, Jinlong Shi

“…However, underwater images frequently face issues, such as color loss and diminished contrast. …”
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A multi-scale cross-dimension interaction approach with adaptive dilated TCN for RUL prediction by Zhe Lu, Bing Li, Changyu Fu, Liang Xu, Bai Jiang, Zelong Li, Junbao Wu, Siye Jia

“…To address the aforementioned issues, this paper proposes an Adaptive Dilated Temporal Convolutional Network (AD-TCN) approach, incorporating a Multi-Scale Cross-Dimension Interaction Module (MSCDIM) to enhance feature extraction and interaction. …”
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WormNet: A Multi-View Network for Silkworm Re-Identification by Hongkang Shi, Minghui Zhu, Linbo Li, Yong Ma, Jianmei Wu, Jianfei Zhang, Junfeng Gao

“…Specifically, we introduce a multi-order feature extraction module that captures a wide range of fine-grained features by utilizing convolutional kernels of varying sizes and parallel cardinality, effectively mitigating issues of high individual similarity and diverse poses. …”
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SR-YOLO: Spatial-to-Depth Enhanced Multi-Scale Attention Network for Small Target Detection in UAV Aerial Imagery by Shasha Zhao, He Chen, Di Zhang, Yiyao Tao, Xiangnan Feng, Dengyin Zhang

“…First, the Space-to-Depth layer and Receptive Field Attention Convolution are combined, and the SR-Conv module is designed to replace the Conv module within the original backbone network. …”
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DVCW-YOLO for Printed Circuit Board Surface Defect Detection by Pei Shi, Yuyang Zhang, Yunqin Cao, Jiadong Sun, Deji Chen, Liang Kuang

“…Next, within the neck structure, the C2f module is substituted with the more lightweight VOVGSCSP module, thereby reducing model redundancy, simplifying model complexity, and enhancing detection speed. …”
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Using Blockchain in the Registration and Authentication of a Carpooling Application: From Review to Proposal by Lina Sofía Cardona Martínez, Cesar Andrés Sandoval Muñoz, Ricardo Salazar-Cabrera, Álvaro Pachón de la Cruz, Juan Manuel Madrid Molina

“…The registration and authentication module developed in this work allows increased security, scalability, and user adoption for any type of application, e.g., carpooling.…”
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Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution by Shizhao Li, Zhichao Yan, Boxiang Ma, Shaoru Guo, Hongxia Song

“…However, existing semantic segmentation methods often suffer from issues such as low precision and slow inference speed. …”
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Research on Method for Intelligent Recognition of Deep-Sea Biological Images Based on PSVG-YOLOv8n by Dali Chen, Xianpeng Shi, Jichao Yang, Xiang Gao, Yugang Ren

“…Finally, an improved detection head synergistically fuses all the modules, yielding substantial enhancements in the overall accuracy. …”
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EMSAM: enhanced multi-scale segment anything model for leaf disease segmentation by Junlong Li, Quan Feng, Jianhua Zhang, Jianhua Zhang, Sen Yang

“…EMSAM employs the Local Feature Extraction Module (LFEM) and the Global Feature Extraction Module (GFEM) to extract local and global features from images respectively. …”
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PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection by Haomeng Guo, Zheming Chai, Huize Dai, Lei Yan, Pengle Cheng, Jianhua Yang

“…In order to improve the algorithm’s ability to detect multi-scale defects, this study introduced the ShareSepHead (Share Separated Head) and C2f_SAC (C2f module with Switchable Atrous Convolution) modules. …”
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Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction by Wei Png Chua, Chien Chern Cheah

“…WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. …”
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Multi-Strategy Enhancement of YOLOv8n Monitoring Method for Personnel and Vehicles in Mine Air Door Scenarios by Lei Zhang, Hongjing Tao, Zhipeng Sun, Weixun Yi

“…Firstly, the Faster Block module, which incorporates partial convolution (PConv), is integrated with the C2f module of the backbone network. …”
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PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion by Heqi Yang, Junming Dong, Cancan Wang, Zhida Lian, Hui Chang

“…First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. The ConvNeXtBlock with inverted bottleneck is introduced in the P4 layer, greatly improving small-target feature capture and semantic understanding. …”
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A lightweight model for echo trace detection in echograms based on improved YOLOv8 by Jungang Ma, Jianfeng Tong, Minghua Xue, Junfan Yao

“…YOLOv8-SBE adds the C2f_ScConv module to improve efficiency and reduce parameters, incorporates the BiFPN structure to enhance information transfer, and uses the EMA attention module for better small target recognition. …”
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Self-attention Deep Field-embedded Factorization Machine for Click-through Rate Prediction by Guangli LI, Yiyuan YE, Guangxin XU, Hongbin ZHANG, Guangting WU, Jingqin LYU

“…However, current CTR prediction models have the following key issues. First, the raw embedding vectors have not been fully refined. …”
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Intelligent analysis of the quality of educational content on statistics of student performance in the learning management system "Scorina" by A. G. Savenko

“…The module is designed to identify the shortcomings in educational content and to improve the materials that could cause the difficulties for students when studying certain issues or topics of the disciplines are studied based on statistical data of student performance. …”
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Synergistic Multi-Granularity Rough Attention UNet for Polyp Segmentation by Jing Wang, Chia S. Lim

“…However, complex backgrounds, diverse polyp morphologies, and ambiguous boundaries make this task difficult. To address these issues, we propose the Synergistic Multi-Granularity Rough Attention U-Net (S-MGRAUNet), which integrates three key modules: the Multi-Granularity Hybrid Filtering (MGHF) module for extracting multi-scale contextual information, the Dynamic Granularity Partition Synergy (DGPS) module for enhancing polyp-background differentiation through adaptive feature interaction, and the Multi-Granularity Rough Attention (MGRA) mechanism for further optimizing boundary recognition. …”
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A Method of Image Restoration for Distortion of Object in Water-Air Cross-Media by Yuhe Gao, Jishen Jia, Lei Cai, Meng Zhou, Haojie Chai, Jinze Jia

“…Finally, the blurring artifact elimination module eliminates artifacts caused by image blurring and geometric line correction by using multiscale fusion of individual U-Net information streams. …”
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