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[Please circulate to all the intersted people / your contacts]
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2 PhD positions (02)
Cognitive Computing
Center for Information Technology
FONDAZIONE BRUNO KESSLER (03)
Duration: 3 years
Close Date: May 27, 2015 (04)
Formal application at:
http://ict.unitn.it/application/project_specific_grants#A3
http://ict.unitn.it/application/project_specific_grants#A4 (05)
These position will be part of the Joint Project "Understanding multimedia
content (UMC)" of the Cognitive Computing research line
(http://ict.fbk.eu/cognitive-computing). The UMC project is developed in
collaboration between the Natural Language Processing research unit
(hlt.fbk.eu) the Technologies for Vision research init (tev.fbk.eu) and the
Data and Knowledge Management research unit (dkm.fbk.eu). (06)
MULTIMEDIA INFORMATION EXTRACTION DRIVEN BY BACKGROUND KNOWLEDGE:
(http://ict.unitn.it/application/project_specific_grants#A3) (07)
This phd has the objective of extracting events from commented videos
exploiting background knowledge available in the semantic web. This phd should
develop a holistic approach, where the process of extracting information from
the video, and from the associated text are integrated and can affect each
other at any stage. This implies that video stream and textual stream are
considered as a whole information space and their interpretations are not
independent. Furthermore, video-text interpretation should not happen in the
knowledge vacuum, but it should exploit the existing large amount of background
knowledge available in the semantic web under the form of ontologies and RDF
data. Nowadays--in contrast with the early years of AI when knowledge
acquisition was a bottleneck--large amount of commonsense knowledge is
available in the semantic web, but it cannot be easily exploited by the
state-of-the-art approaches to video and text analisys. The thesis should
investigate on how to extend and adapt algorithms for video and text analysis
in order to inject background knowledge. The thesis, to reach it's objective,
should combine techniques in machine learning--for processing low level
data--with automated reasoning--to manage with high level semantic knowledge. (08)
VISION FOR MULTIMEDIA UNDERSTANDING:
(http://ict.unitn.it/application/project_specific_grants#A4) (09)
Multimedia content analysis more and more relies on advanced machine learning
to capture the enormous richness of multi-modal sources (commented videos,
images with captions, etc.). At the other side, domain specific knowledge is
often available to leverage the content analysis task, but effectively encoding
it into machine learning (down to the development of task-specific feature
representations) is still an open research issue. The goal of this PhD is to
progress on the computer vision side of the problem, to go beyond a mono-modal
approach where supervisions for learning are provided explicitly. Instead, we
will investigate how structured (background knowledge) and semi-structured data
(e.g. text captions and descriptions) can be used to provide implicit
supervision to enrich the task-specific visual learning capabilities. (010)
SKILLS: Candidates are required to have basic or advanced skills in one or more
of the following areas: artificial intelligence, machine learning, computer
vision, natural language processing and knowledge representation techniques. (011)
For further infos please contact: (012)
Dr. Oswald Lanz
TEV, Fondazione Bruno Kessler
Email: lanz@xxxxxx
Web: tev.fbk.eu/people/profile/lanz (013)
Dr. Bernardo Magnini
HLT, Fondazione Bruno Kessler
Email: magnini@xxxxxx
Web: hlt.fbk.eu/people/profile/magnini (014)
Dr. Luciano Serafini
DKM, Fondazione Bruno Kessler
Email: serafini@xxxxxx
Web: dkm.fbk.eu/people/profile/serafini (015)
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