Giuseppe Vecchio

TL;DR: A video object segmentation framework that leverages the combined advantages of user feedback for segmentation and gamification strategy by simulating multiple game players through a reinforcement learning (RL) model that reproduces human ability to pinpoint moving objects and using the simulated feedback to drive the decisions of a fully convolutional deep segmentation network.

TL;DR: In this paper, a patch-based generative adversarial network (GAN) is proposed to estimate spatially-varying bidirectional reflectance distribution function (SVBRDF) material properties from a single image.

TL;DR: A method to drive the generation of realistic classical order images using GAN approaches anchored to semantic ontology domain representation is proposed to create automated methods for automatically classifying and successively retrieving photographic data leveraging the current breed of AI methods based on Deep Learning paradigm.

TL;DR: A broad overview of the ongoing research portfolio of the Autonomous Agents Research Group is provided and open problems for future directions are discussed.

TL;DR: MIDGARD differs from other major simulation platforms in that it proposes a highly configurable procedural landscape generation pipeline, which enables autonomous agents to be trained in diverse scenarios while reducing the efforts and costs needed to create digital content from scratch.