Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Xiankun Zhang,Jiayang Guo Conference proce

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書目名稱Advanced Intelligent Computing Technology and Applications影響因子(影響力)

書目名稱Advanced Intelligent Computing Technology and Applications影響因子(影響力)學(xué)科排名

書目名稱Advanced Intelligent Computing Technology and Applications網(wǎng)絡(luò)公開度

書目名稱Advanced Intelligent Computing Technology and Applications網(wǎng)絡(luò)公開度學(xué)科排名

書目名稱Advanced Intelligent Computing Technology and Applications被引頻次

書目名稱Advanced Intelligent Computing Technology and Applications被引頻次學(xué)科排名

書目名稱Advanced Intelligent Computing Technology and Applications年度引用

書目名稱Advanced Intelligent Computing Technology and Applications年度引用學(xué)科排名

書目名稱Advanced Intelligent Computing Technology and Applications讀者反饋

書目名稱Advanced Intelligent Computing Technology and Applications讀者反饋學(xué)科排名

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Anomaly Detection of Transmission Line Large Metal Based on EGFPN-YOLO and UAVsYOLO is proposed. The model combines the latest YOLOv8n algorithm, with particular improvements in the detection head, designing an efficient and lightweight detection head called EfficientHead. Additionally, inspired by the RepGFPN method, the Neck part of the model is improved by introducing the C

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AYOLOv8: Improved Detector Based on YOLOv8 to Focus More on Small and Medium Objectseries and has attracted the attention of scholars due to its excellent detection performance. However, this model shows slightly inferior detection performance for small and medium-sized objects. To cope with this issue, in this paper, we propose three effective modules and embed them to YOLOv8 for

只看該作者 · 發(fā)表于 2025-3-23 04:37:32

SFERNet: Student Facial Expression Recognition Using Superpixel-Assisted Global Semantic Enhancementapturing non-local dependencies with semantic continuity and representing fine-grained local features. To tackle these issues, we propose SFERNet, a novel emotion computation network based on semantic-enhanced global dependency modeling and fine-grained feature compensation. Firstly, we introduce a

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