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Titlebook: Advances in Computational Intelligence; 22nd Mexican Interna Hiram Calvo,Lourdes Martínez-Villase?or,Hiram Ponc Conference proceedings 2024

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發(fā)表于 2025-3-21 17:15:23 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Computational Intelligence
期刊簡稱22nd Mexican Interna
影響因子2023Hiram Calvo,Lourdes Martínez-Villase?or,Hiram Ponc
視頻videohttp://file.papertrans.cn/148/147156/147156.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Advances in Computational Intelligence; 22nd Mexican Interna Hiram Calvo,Lourdes Martínez-Villase?or,Hiram Ponc Conference proceedings 2024
影響因子The two-volume set LNAI 14391 and 14392 constitutes the proceedings of the 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, held in Yucatán, Mexico, in November 2023..The total of 49 papers presented in these two volumes was carefully reviewed and selected from 115 submissions..The proceedings of MICAI 2023 are published in two volumes. The first?volume, Advances in Computational Intelligence, contains 24 papers structured?into three sections:.– Machine Learning.– Computer Vision and Image Processing.– Intelligent Systems.The second volume, Advances in Soft Computing, contains 25 papers structured into three sections:.– Natural Language Processing.– Bioinformatics and Medical Applications.– Robotics and Applications.
Pindex Conference proceedings 2024
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Conference proceedings 2024tured?into three sections:.– Machine Learning.– Computer Vision and Image Processing.– Intelligent Systems.The second volume, Advances in Soft Computing, contains 25 papers structured into three sections:.– Natural Language Processing.– Bioinformatics and Medical Applications.– Robotics and Applications.
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https://doi.org/10.1007/978-3-319-55498-3r provides a benchmark comparison between diverse state-of-the-art lightweight models in a facial reconstruction model, with the aim to reduce its computational complexity so that it can be tested on a mobile device.
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https://doi.org/10.1007/978-3-319-55498-3ene underlying surfaces. Through state-of-the-art performance on standard real-images benchmarks, we show that the proposed methods, coined as TV-NeRF and TGV-NeRF, make strong baselines in novel view synthesis with few training views.
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