5th – 9th July 2021
PAISS 2021 is an international summer school in AI organised by the 3IA institutes PRAIRIE, MIAI and in particular by INRIA Grenoble (Rhone-Alpes) and NAVER LABS Europe. This year’s event will be held online.
Yannis Kalantidis and Diane Larlus on the scientific committee.
Social guests for 2021 include:
Yannis Avrithis (Inria), Cécile Boulard (NAVER LABS Europe), Gabriela Csurka (NAVER LABS Europe), Dima Damen (University of Bristol), Kosta Derpanis (York University/Samsung AI Centre Toronto), Mohamed Elhoseiny (KAUST), Hady Elsahar (NAVER LABS Europe), Elisa Fromont (Université Rennes 1/IRISA/Inria), Adrien Gaidon (Toyota Research Institute), Efstratios Gavves (University of Amsterdam), Xavier Giro-i-Nieto (UPC Barcelona), Fatma Guney (Koc University), Torsten Sattler (Czech Technical University in Prague), Marie Sacksick (CybelAngel), Stavros Tsogkas (Samsung), Gul Varol (Ecole des Ponts ParisTech).
Social program (Gathertown)
Speakers:
- Lourdes Agapito is a professor of 3D vision in the Department of Computer Science at University College London (UCL).Stéphanie Allassonnière is a professor of Applied Mathematics and Statistics at the School of Medicine, University of Paris.
- Francis Bach is a researcher at Inria, where he heads the SIERRA research team, and an adjunct professor at École Normale Supérieure.
- Alejandrina Cristià is a CNRS research director in the Laboratoire de Sciences Cognitives et Psycholinguistique at ENS.Andrew Davison is a professor of robot vision at Imperial College London, where he also leads the Dyson Robotics Laboratory.
- Pascale Fung is a professor at Hong Kong University of Science & Technology, and director of HKUST Center for AI Research.
- Arthur Gretton is a professor with the Gatsby Computational Neuroscience Unit, and director of the Centre for Computational Statistics and Machine Learning at UCL.
- Hannah Kerner is an assistant research professor at the University of Maryland, College Park and the machine learning lead and U.S. domestic co-lead for NASA Harvest.
- Yann LeCun is VP and Chief AI Scientist at Facebook, and Silver Professor of Computer Science, Data Science, Neural Science, and Electrical Engineering at NYU.
- Catherine Nakalembe is an assistant research professor at the University of Maryland and the NASA Harvest Africa program director.
- Cordelia Schmid is a research director at Inria Paris in the Willow research team, and a researcher at Google.
- Jean-Philippe Vert is a research scientist at Google Brain and an adjunct research professor at PSL Mines ParisTech.
Related Content
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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