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Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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發(fā)表于 2025-3-21 19:05:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Computer Vision – ECCV 2020
副標(biāo)題16th European Confer
編輯Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm
視頻videohttp://file.papertrans.cn/235/234224/234224.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur
描述The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..?..?.
出版日期Conference proceedings 2020
關(guān)鍵詞computer networks; computer vision; education; face recognition; image analysis; image coding; image proce
版次1
doihttps://doi.org/10.1007/978-3-030-58610-2
isbn_softcover978-3-030-58609-6
isbn_ebook978-3-030-58610-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

書(shū)目名稱(chēng)Computer Vision – ECCV 2020影響因子(影響力)




書(shū)目名稱(chēng)Computer Vision – ECCV 2020影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2020網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Computer Vision – ECCV 2020網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2020被引頻次




書(shū)目名稱(chēng)Computer Vision – ECCV 2020被引頻次學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2020年度引用




書(shū)目名稱(chēng)Computer Vision – ECCV 2020年度引用學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2020讀者反饋




書(shū)目名稱(chēng)Computer Vision – ECCV 2020讀者反饋學(xué)科排名




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H3DNet: 3D Object Detection Using Hybrid Geometric Primitives,
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Generative Low-Bitwidth Data Free Quantization,ization (GDFQ) to remove the data dependence burden. Specifically, we propose a knowledge matching generator to produce meaningful fake data by exploiting classification boundary knowledge and distribution information in the pre-trained model. With the help of generated data, we can quantize a model
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Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues,he forgery patterns via our two-stream collaborative learning framework. We apply DCT as the applied frequency-domain transformation. Through comprehensive studies, we show that the proposed F.-Net significantly outperforms competing state-of-the-art methods on all compression qualities in the chall
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SeqHAND: RGB-Sequence-Based 3D Hand Pose and Shape Estimation,rent layer of the framework during domain finetuning from synthetic to real allows preservation of the visuo-temporal features learned from sequential synthetic hand images. Hand poses that are sequentially estimated consequently produce natural and smooth hand movements which lead to more robust es
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Rethinking the Distribution Gap of Person Re-identification with Camera-Based Batch Normalization, undervalued before due to the lack of cross-camera information, to achieve competitive ReID performance. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach. The code is available at ..
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