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发表于 2025-3-21 17:15:23
书目名称Advances in Computational Intelligence影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0147156<br><br> <br><br>书目名称Advances in Computational Intelligence影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0147156<br><br> <br><br>书目名称Advances in Computational Intelligence网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0147156<br><br> <br><br>书目名称Advances in Computational Intelligence网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0147156<br><br> <br><br>书目名称Advances in Computational Intelligence被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0147156<br><br> <br><br>书目名称Advances in Computational Intelligence被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0147156<br><br> <br><br>书目名称Advances in Computational Intelligence年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0147156<br><br> <br><br>书目名称Advances in Computational Intelligence年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0147156<br><br> <br><br>书目名称Advances in Computational Intelligence读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0147156<br><br> <br><br>书目名称Advances in Computational Intelligence读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0147156<br><br> <br><br>
Guileless
发表于 2025-3-21 21:18:29
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Prosaic
发表于 2025-3-22 01:52:05
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MITE
发表于 2025-3-22 05:41:50
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.
Nuance
发表于 2025-3-22 12:18:10
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不在灌木丛中
发表于 2025-3-22 13:03:25
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.
triptans
发表于 2025-3-22 19:38:45
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.
maintenance
发表于 2025-3-22 23:14:33
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光明正大
发表于 2025-3-23 04:24:55
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种类
发表于 2025-3-23 07:19:13
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