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

Baleful

书目名称Computer Vision – ECCV 2018影响因子(影响力)




书目名称Computer Vision – ECCV 2018影响因子(影响力)学科排名




书目名称Computer Vision – ECCV 2018网络公开度




书目名称Computer Vision – ECCV 2018网络公开度学科排名




书目名称Computer Vision – ECCV 2018被引频次




书目名称Computer Vision – ECCV 2018被引频次学科排名




书目名称Computer Vision – ECCV 2018年度引用




书目名称Computer Vision – ECCV 2018年度引用学科排名




书目名称Computer Vision – ECCV 2018读者反馈




书目名称Computer Vision – ECCV 2018读者反馈学科排名

率直

evasive

Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead V in synthetic environments. However, model-free DRL models do not consider the dynamics in the real-world environments, and they often fail to generalize to new scenes. In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices—We propose a novel,

唤起

Improving Deep Visual Representation for Person Re-identification by Global and Local Image-languageverse auxiliary information has been utilized to improve the visual feature learning. In this paper, we propose to exploit natural language description as additional training supervisions for effective visual features. Compared with other auxiliary information, language can describe a specific perso

deadlock

Learning 3D Shapes as Multi-layered Height-Maps Using 2D Convolutional NetworksLH) where at each grid location, we store multiple instances of height maps, thereby representing 3D shape detail that is hidden behind several layers of occlusion. We provide a novel view merging method for combining view dependent information (Eg. MLH descriptors) from multiple views. Because of t

neuron

A Geometric Perspective on Structured Light CodingHamiltonian SL coding, a novel family of SL coding schemes that can recover 3D shape with high precision, with only a small number (as few as three) of images. We establish structural similarity between popular discrete (binary) SL coding methods, and Hamiltonian coding, which is a continuous coding

neuron

Ingenuity

愤怒事实

Super-Resolution and Sparse View CT Reconstructionedded in 3D volumes. To reconstruct such structures at resolutions below the Nyquist limit of the CT image sensor, we devise a new 3D structure tensor prior, which can be incorporated as a regularizer into more traditional proximal optimization methods for CT reconstruction. As a second, smaller con

Barter