antibody
发表于 2025-3-21 19:44:40
书目名称Computer Vision – ECCV 2016影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0234176<br><br> <br><br>书目名称Computer Vision – ECCV 2016影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0234176<br><br> <br><br>书目名称Computer Vision – ECCV 2016网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0234176<br><br> <br><br>书目名称Computer Vision – ECCV 2016网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0234176<br><br> <br><br>书目名称Computer Vision – ECCV 2016被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0234176<br><br> <br><br>书目名称Computer Vision – ECCV 2016被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0234176<br><br> <br><br>书目名称Computer Vision – ECCV 2016年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0234176<br><br> <br><br>书目名称Computer Vision – ECCV 2016年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0234176<br><br> <br><br>书目名称Computer Vision – ECCV 2016读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0234176<br><br> <br><br>书目名称Computer Vision – ECCV 2016读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0234176<br><br> <br><br>
Hormones
发表于 2025-3-21 22:48:49
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神刊
发表于 2025-3-22 02:22:04
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orthopedist
发表于 2025-3-22 05:02:55
https://doi.org/10.1007/978-981-13-1141-3ulti-view dataset depicting plants with known 3D structures and topologies undergoing small nonrigid motion. Our method outperforms a state-of-the-art 3D reconstruction method designed for non-moving thin structure. We evaluate under several common measures, and we propose a new measure for reconstr
提升
发表于 2025-3-22 10:49:47
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线
发表于 2025-3-22 13:38:47
https://doi.org/10.1007/978-3-319-66002-8log-likelihood. Firstly, we illustrate the benefits of the model on a synthetic dataset. The model is then evaluated in both uni- and multi-modality settings on two different classification tasks with off-the-shelf convolutional neural network (CNN) features which generate state-of-the-art results w
线
发表于 2025-3-22 20:22:05
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Disk199
发表于 2025-3-22 22:11:22
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uncertain
发表于 2025-3-23 04:35:38
Detecting Text in Natural Image with Connectionist Text Proposal Network1 F-measure on the ICDAR 2013 and 2015 benchmarks, surpassing recent results [., .] by a large margin. The CTPN is computationally efficient with 0.14 s/image, by using the very deep VGG16 model [.]. Online demo is available: ..
Flinch
发表于 2025-3-23 06:42:22
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