Radiomics converts medical images into mineable data via a high-throughput extraction of
quantitative features used for clinical decision support. However, these radiomic features are …

In this article, we perform an exhaustive analysis of the use of Artificial Neural Networks and
Deep Learning in the Visual Arts. We begin by introducing changes in Artificial Intelligence …

Existing image classification datasets used in computer vision tend to have a uniform
distribution of images across object categories. In contrast, the natural world is heavily …

Can we detect common objects in a variety of image domains without instance-level
annotations? In this paper, we present a framework for a novel task, cross-domain weakly …

The Deepfake phenomenon has become very popular nowadays thanks to the possibility to
create incredibly realistic images using deep learning tools, based mainly on ad-hoc …

Humans have long been recorded in a variety of forms since antiquity. For example,
sculptures and paintings were the primary media for depicting human beings before the …

In the past few years, a lot of work has been done towards reconstructing the 3D facial
structure from single images by capitalizing on the power of Deep Convolutional Neural …

We introduce a novel unsupervised domain adaptation approach for object detection. We
aim to alleviate the imperfect translation problem of pixel-level adaptations, and the source …

We present a novel large-scale dataset and accompanying machine learning models aimed
at providing a detailed understanding of the interplay between visual content, its emotional …

We present a method for training a regression network from image pixels to 3D morphable
model coordinates using only unlabeled photographs. The training loss is based on features …