Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Substantial efforts have been devoted more recently to presenting various methods for
object detection in optical remote sensing images. However, the current survey of datasets …

Image segmentation is often ambiguous at the level of individual image patches and
requires contextual information to reach label consensus. In this paper we introduce …

Spatial pyramid pooling module or encode-decoder structure are used in deep neural
networks for semantic segmentation task. The former networks are able to encode multi …

A central goal of machine learning is the development of systems that can solve many
problems in as many data domains as possible. Current architectures, however, cannot be …

In this paper, we study the context aggregation problem in semantic segmentation.
Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we …

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 …

We consider image transformation problems, where an input image is transformed into an
output image. Recent methods for such problems typically train feed-forward convolutional …

Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …

Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the
key problems in computer vision. Despite the community's efforts in data collection, there are …