Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

柔聲地說

978-3-030-58525-9Springer Nature Switzerland AG 2020

樣式

M. D. Amarasinghe,S. Balasubramaniame available as ground truths. Recently, there have been some approaches that incorporate the problem setting of non-rigid structure-from-motion (NRSfM) into deep learning to learn 3D structure reconstruction. The most important difficulty of NRSfM is to estimate both the rotation and deformation at

非秘密

jabber

https://doi.org/10.1007/978-94-011-4102-4 high-resolution images at high frame rates, which generates bandwidth and memory issues. By capturing only changes in the brightness with a very low latency and at low data rate, event-based cameras have the ability to tackle such issues. In this paper, we present a new framework that retrieves den

防水

https://doi.org/10.1007/978-94-011-4102-4cess of higher-order assignment methods, has sparked an interest in the search for improved higher-order matching algorithms on warped images due to projection. Although, currently, several existing methods “flatten” such 3D images to use planar graph/hypergraph matching methods, they still suffer f

標(biāo)準(zhǔn)

A簡潔的

I. Pavlik,J.O. Falkinham III,J. Kazdabject detection, we introduce a single-stage and multi-scale framework to learn a unified representation for objects within different distance ranges, termed as UR3D. UR3D formulates different tasks of detection by exploiting the scale information, to reduce model capacity requirement and achieve ac

大炮

https://doi.org/10.1007/978-3-030-72854-0versity of scene texts in scale, orientation, shape and aspect ratio, as well as the inherent limitation of convolutional neural network for geometric transformations, to achieve accurate scene text detection is still an open problem. In this paper, we propose a novel sequential deformation method t

啤酒

植物學(xué)

Micael Jonsson,Ryan A. Sponselleroped recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation n