NAVER LABS Europe seminars are open to the public. This seminar is virtual and requires registration.
Date: 14th January 2021, 10am (GMT +01.00)
Speaker: Alessio Del Bue is a tenured senior researcher leading the PAVIS (Pattern Analyisis and computer VISion) research line of the Italian Institute of Technology (IIT) in Genova, Italy. Previously, he was a researcher in the Institute for Systems and Robotics at the Instituto Superior Técnico (IST) in Lisbon, Portugal. Before that, he obtained his Ph.D. under the supervision of Dr. Lourdes Agapito in the Department of Computer Science at Queen Mary University of London. His current research interests are related to 3D scene understanding from multi-modal input (images, depth, audio) to support the development of assistive Artificial Intelligence systems. He is co-author of more than 100 scientific publications, in refereed journals and international conferences, member of the technical committees of important computer vision conferences (CVPR, ICCV, ECCV, BMVC, etc.), and he serves as an associate editor of Patter Recognition and Computer Vision and Image Understanding journals. Finally, Dr. Del Bue is an IEEE and ELLIS member.
Abstract: In this talk I will present the current activities of Pattern Analysis and Computer Vision (PAVIS) Lab at the Italian Institute of Technology (IIT) focusing on our newly developed infrastructure for Assistive AI. After a brief introduction on the hardware and computational architecture, I will present our contributions related to Social AI, modelling and forecasting human interaction in real world scenarios, and Spatial AI, understanding and reasoning over the semantic of 3D scenes. In details, I will present first our recent efforts on Visual Social Distancing for monitoring resilient spaces (Social AI) where we deployed methods for automatically assessing the inter-personal distance among people using uncalibrated cameras in real scenarios. Then, related to Spatial AI, the talk will present active vision approaches for autonomous 3D reconstruction, exploration and localisation in unconstrained real-word scenarios with applications to AR and human-robot interaction.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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