Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …

Graphics processing units (GPUs) are used today in a wide range of applications, mainly
because they can dramatically accelerate parallel computing, are affordable and energy …

In this work we address the task of semantic image segmentation with Deep Learning and
make three main contributions that are experimentally shown to have substantial practical …

This paper presents cosmological results based on full-mission Planck observations of
temperature and polarization anisotropies of the cosmic microwave background (CMB) …

Generally, convolutional neural networks (CNNs) process data on a regular grid, eg, data
generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input …

We test the statistical isotropy and Gaussianity of the cosmic microwave background (CMB)
anisotropies using observations made by the Planck satellite. Our results are based mainly …

It is often a difficult task to accurately segment images with intensity inhomogeneity, because
most of representative algorithms are region-based that depend on intensity homogeneity of …

We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of
the Neural Process family that models translation equivariance in the data. Translation …

There is a strong and growing need in the biology research community for accurate,
automated image analysis. Here, we describe CellProfiler 2.0, which has been engineered …

We present a new approach for performing high-quality edge-preserving filtering of images
and videos in real time. Our solution is based on a transform that defines an isometry …