Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw

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

indignant

缺陷

Super-Identity Convolutional Neural Network for Face Hallucinationy metric for faces from these two domains. Extensive experimental evaluations demonstrate that the proposed SICNN achieves superior visual quality over the state-of-the-art methods on a challenging task to super-resolve 12?.?14 faces with an 8. upscaling factor. In addition, SICNN significantly impr

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CLOT

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3input images while adding photorealism and retaining identity information. We combine face images generated by the proposed method with a real data set to train face recognition algorithms and evaluate the model quantitatively on two challenging data sets: LFW and IJB-A. The generated images by our

faculty

HairNet: Single-View Hair Reconstruction Using Convolutional Neural Networks continuous representation for hairstyles, which allows us to interpolate naturally between hairstyles. We use a large set of rendered synthetic hair models to train our network. Our method scales to real images because an intermediate 2D orientation field, automatically calculated from the real ima

faculty

形容詞

BALK

Populations of Small Solar System Bodies,viors are heavily influenced by known areas in the images (., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net’s abi

入會

https://doi.org/10.1057/9780333982921y metric for faces from these two domains. Extensive experimental evaluations demonstrate that the proposed SICNN achieves superior visual quality over the state-of-the-art methods on a challenging task to super-resolve 12?.?14 faces with an 8. upscaling factor. In addition, SICNN significantly impr