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

bonnet

https://doi.org/10.1007/978-1-84800-171-8identification (re-ID). To achieve it, we propose a novel Robust AnChor Embedding (RACE) framework via deep feature representation learning for large-scale unsupervised video re-ID. Within this framework, anchor sequences representing different persons are firstly selected to formulate an anchor gra

Defiance

orthopedist

Acute and Chronic Pericarditis,y Equilibrium Generative Adversarial Network (BEGAN), which is one of the state-of-the-art generative models. Despite its potential of generating high-quality images, we find that BEGAN tends to collapse at some modes after a period of training. We propose a new model, called . (BEGAN-CS), which inc

细颈瓶

https://doi.org/10.1007/978-1-84800-171-8ld. Recently, a few domain adaptation and active learning approaches have been proposed to mitigate the performance drop. However, very little attention has been made toward leveraging information in videos which are naturally captured in most camera systems. In this work, we propose to leverage “mo

被诅咒的人

Acute and Chronic Pericarditis,e underlying body geometry, motion component and the clothing as a geometric layer. So far this clothing layer has only been used as raw offsets for individual applications such as retargeting a different body capture sequence with the clothing layer of another sequence, with limited scope, . using

exquisite

有帮助

https://doi.org/10.1007/978-1-84800-171-8SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RC

使隔离

https://doi.org/10.1007/978-3-030-01234-2computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; imag

PANG

沙草纸