Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Object segmentation is a crucial problem that is usually solved by using supervised learning
approaches over very large datasets composed of both images and corresponding object …

We introduce a novel framework to build a model that can learn how to segment objects from
a collection of images without any human annotation. Our method builds on the observation …

We introduce MOVE, a novel method to segment objects without any form of supervision.
MOVE exploits the fact that foreground objects can be shifted locally relative to their initial …

Abstract We present Deep Region Competition (DRC), an algorithm designed to extract
foreground objects from images in a fully unsupervised manner. Foreground extraction can …

The need for accurate yield estimates for viticulture is becoming more important due to
increasing competition in the wine market worldwide. One of the most promising methods to …

The recent enthusiasm for open-world vision systems show the high interest of the
community to perform perception tasks outside of the closed-vocabulary benchmark setups …

We propose ComGAN, a simple unsupervised generative model, which simultaneously
generates realistic images and high semantic masks under an adversarial loss and a binary …

Foreign object intrusion is a great threat to high-speed railway safety operations. Accurate
foreign object intrusion detection is particularly important. As a result of the lack of intruding …