hexagon
发表于 2025-3-21 17:04:47
书目名称Computer Vision – ECCV 2024影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0242317<br><br> <br><br>书目名称Computer Vision – ECCV 2024影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0242317<br><br> <br><br>书目名称Computer Vision – ECCV 2024网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0242317<br><br> <br><br>书目名称Computer Vision – ECCV 2024网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0242317<br><br> <br><br>书目名称Computer Vision – ECCV 2024被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0242317<br><br> <br><br>书目名称Computer Vision – ECCV 2024被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0242317<br><br> <br><br>书目名称Computer Vision – ECCV 2024年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0242317<br><br> <br><br>书目名称Computer Vision – ECCV 2024年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0242317<br><br> <br><br>书目名称Computer Vision – ECCV 2024读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0242317<br><br> <br><br>书目名称Computer Vision – ECCV 2024读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0242317<br><br> <br><br>
lactic
发表于 2025-3-21 22:06:53
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Acetabulum
发表于 2025-3-22 02:28:37
,CanonicalFusion: Generating Drivable 3D Human Avatars from Multiple Images,tegrating individual reconstruction results into the canonical space. To be specific, we first predict Linear Blend Skinning (LBS) weight maps and depth maps using a shared-encoder-dual-decoder network, enabling direct canonicalization of the 3D mesh from the predicted depth maps. Here, instead of p
单片眼镜
发表于 2025-3-22 05:00:52
,Camera Height Doesn’t Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimatioust from regular training data, .., driving videos. We refer to this training framework as FUMET. The key idea is to leverage cars found on the road as sources of scale supervision and to incorporate them in network training robustly. FUMET detects and estimates the sizes of cars in a frame and aggr
conservative
发表于 2025-3-22 08:49:34
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fulmination
发表于 2025-3-22 16:43:52
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fulmination
发表于 2025-3-22 19:06:12
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atopic-rhinitis
发表于 2025-3-22 23:38:03
,GENIXER: Empowering Multimodal Large Language Model as a Powerful Data Generator,nerate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce ., a comprehensive data generation pipeline consisting of four key steps: (i) instruction data collection, (ii) instruction te
生来
发表于 2025-3-23 02:59:32
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committed
发表于 2025-3-23 08:52:29
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