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

Intruder

Deep Boosting for Image Denoising existing boosting algorithms are surpassed by the emerging learning-based models. In this paper, we propose a novel deep boosting framework (DBF) for denoising, which integrates several convolutional networks in a feed-forward fashion. Along with the integrated networks, however, the depth of the b

biosphere

insomnia

K-convexity Shape Priors for Segmentationsubsets. Since an arbitrary shape can always be divided into convex parts, our regularization model restricts the number of such parts. Previous .-part shape priors are limited to disjoint parts. For example, one approach segments an object via optimizing its . coverage by disjoint convex parts, whi

谁在削木头

啜泣

Legion

olfction

Fighting Fake News: Image Splice Detection via Learned Self-Consistencyver, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs. The algorithm uses the automatically recorded

EVEN

无情

CBC471

CAR-Net: Clairvoyant Attentive Recurrent Networknt. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction ta