Titlebook: Computer Vision – ECCV 2018 Workshops; Munich, Germany, Sep Laura Leal-Taixé,Stefan Roth Conference proceedings 2019 Springer Nature Switze

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書(shū)目名稱Computer Vision – ECCV 2018 Workshops影響因子(影響力)




書(shū)目名稱Computer Vision – ECCV 2018 Workshops影響因子(影響力)學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2018 Workshops網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Computer Vision – ECCV 2018 Workshops網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2018 Workshops被引頻次




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書(shū)目名稱Computer Vision – ECCV 2018 Workshops年度引用學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2018 Workshops讀者反饋




書(shū)目名稱Computer Vision – ECCV 2018 Workshops讀者反饋學(xué)科排名

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樂(lè)章

MobileFace: 3D Face Reconstruction with Efficient CNN Regressiond costly methods preventing real-time applications. In this work we design a compact and fast CNN model enabling real-time face reconstruction on mobile devices. For this purpose, we first study more traditional but slow morphable face models and use them to automatically annotate a large set of ima

intolerance

圖畫(huà)文字

Non-rigid 3D Shape Registration Using an Adaptive Templated; (2) an adaptive shape template method to improve the convergence of registration algorithms and achieve a better final shape correspondence and (3) a new iterative registration method that combines Iterative Closest Points with Coherent Point Drift (CPD) to achieve a more stable and accurate corr

粗魯性質(zhì)

3D Human Body Reconstruction from a Single Image via Volumetric Regressionsion. The proposed method does not require the fitting of a shape model and can be trained to work from a variety of input types, whether it be landmarks, images or segmentation masks. Additionally, non-visible parts, either self-occluded or otherwise, are still reconstructed, which is not the case

粗魯性質(zhì)

自然環(huán)境

儲(chǔ)備

MoQA – A Multi-modal Question Answering Architecturetbook Question Answering (TQA) focuses on questions based on the school curricula, where the text and diagrams are extracted from textbooks. A subset of questions cannot be answered solely based on diagrams, but requires external knowledge of the surrounding text. In this work, we propose a novel de

machination

Quantifying the Amount of Visual Information Used by Neural Caption Generatorsmage foils is reported, showing that the extent to which image captioning architectures retain and are sensitive to visual information varies depending on the type of word being generated and the position in the caption as a whole. We motivate this work in the context of broader goals in the field t