LEAP

LEarning from our collective visual memory to Analyze its trends and Predict future events

 Coordinatore INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE 

Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.

 Nazionalità Coordinatore France [FR]
 Totale costo 1˙496˙736 €
 EC contributo 1˙496˙736 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2013-StG
 Funding Scheme ERC-SG
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-11-01   -   2019-10-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE

 Organization address address: Domaine de Voluceau, Rocquencourt
city: LE CHESNAY Cedex
postcode: 78153

contact info
Titolo: Dr.
Nome: Josef
Cognome: Sivic
Email: send email
Telefono: +33 1 3963 5548

FR (LE CHESNAY Cedex) hostInstitution 1˙496˙736.00
2    INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE

 Organization address address: Domaine de Voluceau, Rocquencourt
city: LE CHESNAY Cedex
postcode: 78153

contact info
Titolo: Ms.
Nome: Catherine
Cognome: Zimmermann
Email: send email
Telefono: +33 1 39635901

FR (LE CHESNAY Cedex) hostInstitution 1˙496˙736.00

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

dynamic    predict    models    events    distributed    sources    experiences    data    scenes    visual    people    tools    first    anticipate    past   

 Obiettivo del progetto (Objective)

'People constantly draw on past visual experiences to anticipate future events and better understand, navigate, and interact with their environment, for example, when seeing an angry dog or a quickly approaching car. Currently there is no artificial system with a similar level of visual analysis and prediction capabilities. LEAP is a first step in that direction, leveraging the emerging collective visual memory formed by the unprecedented amount of visual data available in public archives, on the Internet and from surveillance or personal cameras - a complex evolving net of dynamic scenes, distributed across many different data sources, and equipped with plentiful but noisy and incomplete metadata. The goal of this project is to analyze dynamic patterns in this shared visual experience in order (i) to find and quantify their trends; and (ii) learn to predict future events in dynamic scenes. With ever expanding computational resources and this extraordinary data, the main scientific challenge is now to invent new and powerful models adapted to its scale and its spatio-temporal, distributed and dynamic nature. To address this challenge, we will first design new models that generalize across different data sources, where scenes are captured under vastly different imaging conditions. Next, we will develop a framework for finding, describing and quantifying trends that involve measuring long-term changes in many related scenes. Finally, we will develop a methodology and tools for synthesizing complex future predictions from aligned past visual experiences. Breakthrough progress on these problems would have profound implications on our everyday lives as well as science and commerce, with safer cars that anticipate the behavior of pedestrians on streets; tools that help doctors monitor, diagnose and predict patients’ health; and smart glasses that help people react in unfamiliar situations enabled by the advances from this project.'

Altri progetti dello stesso programma (FP7-IDEAS-ERC)

ASSEMBLYNMR (2014)

3D structures of bacterial supramolecular assemblies by solid-state NMR

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REDUNDANCY (2013)

Functional redundancy of bacterial communities in the laboratory and in the wild

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IIP (2014)

Individualized Implant Placement

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