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

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发表于 2025-3-21 17:15:23 | 显示全部楼层 |阅读模式
期刊全称Advances in Computational Intelligence
期刊简称22nd Mexican Interna
影响因子2023Hiram Calvo,Lourdes Martínez-Villaseñor,Hiram Ponc
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Advances in Computational Intelligence; 22nd Mexican Interna Hiram Calvo,Lourdes Martínez-Villaseñor,Hiram Ponc Conference proceedings 2024
影响因子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.
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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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