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

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https://doi.org/10.1007/978-3-322-90132-3e fusion is presented to reduce feature redundancy caused by the feature pyramid. Extensive experiments on MS COCO-2017 dataset show that AYOLOv8 significantly improves the performance for detecting small and medium-sized objects and achieves lower model complexity and better detection accuracy than
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https://doi.org/10.1007/978-3-322-85675-3gonal constraint and a sparse constraint simultaneously, ensuring the preservation of key information from the original data while enhancing robustness against noise. Extensive experiments on four real-world databases demonstrate the competitiveness of SDGE against state-of-the-art feature extractio
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https://doi.org/10.1007/978-3-322-85675-3ting the problem of high inter class similarity. A Multi-Scale Additive Enhanced Network (MSAENet) is proposed based on MSAE Block and Bridging Residual Module (BRM) and is validated on two datasets, WHU-SIRI and AID. Based on experimental data, the classification accuracy of MSAENet is better than
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https://doi.org/10.1007/978-3-663-10215-1ifferent models as the basic models for SeWi. We also analyze the effective range of hyperparameters for this segmentation method. The results indicate that SeWi exhibits varying degrees of improvement for different models. Of particular note, using ResNet18 as the basic model for SeWi, the accuracy
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https://doi.org/10.1007/978-3-7091-8415-8orithm can quickly and effectively detect students’ distraction phenomena, including daydreaming, distraction, and hidden activities like using mobile phones in blind spots of the camera’s visual capture. This research is significant for helping teachers evaluate students’ performance in online clas
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,Anwendungen der Integralsätze, Luftkräfte, models, aiming to minimize the discrepancy among different model outputs. For task consistency, we promote consistency between the segmentation maps and the pixel-level probability maps transformation from the signed distance maps (SDM), thereby constructing the geometric contours of the target to
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Die Portfolioanalyse der Geldnachfragections. Through extensive experimentation, we demonstrate that our approach surpasses the reported State-Of-The-Art (SOTA) model in Polyphonic Sound Detection Score-scenario1 (PSDS.) and achieves a comparable result in Polyphonic Sound Detection Score-scenario2 (PSDS.) on the DCASE Challenge Task 4.
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