Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Nowadays, Deep Neural Networks are among the main tools used in various sciences.
Convolutional Neural Network is a special type of DNN consisting of several convolution …

Current state-of-the-art methods for image segmentation form a dense image representation
where the color, shape and texture information are all processed together inside a deep …

We propose a novel deep learning-based framework to tackle the challenge of semantic
segmentation of large-scale point clouds of millions of points. We argue that the organization …

Abstract Graph Convolutional Neural Networks (Graph CNNs) are generalizations of
classical CNNs to handle graph data such as molecular data, point could and social …

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 …

Convolutions are the fundamental building blocks of CNNs. The fact that their weights are
spatially shared is one of the main reasons for their widespread use, but it is also a major …

In computer vision, superpixels have been widely used as an effective way to reduce the
number of image primitives for subsequent processing. But only a few attempts have been …

Convolutional neural networks have been applied to a wide variety of computer vision tasks.
Recent advances in semantic segmentation have enabled their application to medical …

Superpixels group perceptually similar pixels to create visually meaningful entities while
heavily reducing the number of primitives for subsequent processing steps. As of these …