L3VISU

Life Long Learning for Visual Scene Understanding (L3ViSU)

Coordinatore	Institute of Science and Technology Austria    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	Austria [AT]
Totale costo	1˙464˙711 €
EC contributo	1˙464˙711 €
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-2012-StG_20111012
Funding Scheme	ERC-SG
Anno di inizio	2013
Periodo (anno-mese-giorno)	2013-01-01   -   2017-12-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	Institute of Science and Technology Austria  Organization address address: Am Campus 1 city: Klosterneuburg postcode: 3400 contact info Titolo: Dr. Nome: Christoph Cognome: Lampert Email: send email Telefono: +43 2243 9000 3101 Fax: +43 2243 9000 2000	AT (Klosterneuburg)	hostInstitution	1˙464˙711.60
2	Institute of Science and Technology Austria  Organization address address: Am Campus 1 city: Klosterneuburg postcode: 3400 contact info Titolo: Mrs. Nome: Carla Cognome: Mazuheli-Chibidziura Email: send email Telefono: +43 2243 9000 1038 Fax: +43 2243 9000 2000	AT (Klosterneuburg)	hostInstitution	1˙464˙711.60

Mappa

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 Obiettivo del progetto (Objective)

'My goal in the project is to develop and analyze algorithms that use continuous, open-ended machine learning from visual input data (images and videos) in order to interpret visual scenes on a level comparable to humans.

L3ViSU is based on the hypothesis that we can only significantly improve the state of the art in computer vision algorithms by giving them access to background and contextual knowledge about the visual world, and that the most feasible way to obtain such knowledge is by extracting it (semi-) automatically from incoming visual stimuli. Consequently, at the core of L3ViSU lies the idea of life-long visual learning.

Sufficient data for such an effort is readily available, e.g. through digital TV-channels and media- sharing Internet platforms, but the question of how to use these resources for building better computer vision systems is wide open. In L3ViSU we will rely on modern machine learning concepts, representing task-independent prior knowledge as prior distributions and function regularizers. This functional form allows them to help solving specific tasks by guiding the solution to 'reasonable' ones, and to suppress mistakes that violate 'common sense'. The result will not only be improved prediction quality, but also a reduction in the amount of manual supervision necessary, and the possibility to introduce more semantics into computer vision, which has recently been identified as one of the major tasks for the next decade.

L3ViSU is a project on the interface between computer vision and machine learning. Solving it requires expertise in both areas, as it is represented in my research group at IST Austria. The life-long learning concepts developed within L3ViSU, however, will have impact outside of both areas, let it be as basis of life-long learning system with a different focus, such as in bioinformatics, or as a foundation for projects of commercial value, such as more intelligent driver assistance or video surveillance systems.'