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

outskirts

Snap Angle Prediction for 360, Panoramasage may enable content-aware projection with fewer perceptible distortions. Whereas existing approaches assume the viewpoint is fixed, intuitively some viewing angles within the sphere preserve high-level objects better than others. To discover the relationship between these optimal . and the spheri

crescendo

commune

DF-Net: Unsupervised Joint Learning of Depth and Flow Using Cross-Task Consistencyled video sequences. Existing unsupervised methods often exploit brightness constancy and spatial smoothness priors to train depth or flow models. In this paper, we propose to leverage geometric consistency as additional supervisory signals. Our core idea is that for rigid regions we can use the pre

懶惰民族

Rejuvenate

Transductive Centroid Projection for Semi-supervised Large-Scale Recognitiononal complexity when collaborating with Convolutional Neural Networks. To this end, we design a simple but effective learning mechanism that merely substitutes the last fully-connected layer with the proposed Transductive Centroid Projection (TCP) module. It is inspired by the observation of the wei

怪物

FECT

Into the Twilight Zone: Depth Estimation Using Joint Structure-Stereo Optimizationenoising approach – which we show to be ineffective for stereo due to its artefacts and the questionable use of the PSNR metric, we propose to instead rely on structures comprising of piecewise constant regions and principal edges in the given image, as these are the important regions for extracting

灌輸

Recycle-GAN: Unsupervised Video Retargetingative to a domain, i.e., if contents of John Oliver’s speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert’s style. Our approach combines both spatial and temporal information along with adversarial losses for content translation and style

Crepitus

香料

Open Set Domain Adaptation by Backpropagatione proposed for closed-set scenario, where the source and the target domain completely share the class of their samples. However, in practice, a target domain can contain samples of classes that are not shared by the source domain. We call such classes the “unknown class” and algorithms that work wel