Objective
A massive and ever growing amount of digital image and video content
is available today, on sites such as
Flickr and YouTube, in audiovisual archives such as those of BBC and
INA, and in personal collections. In most cases, it comes with
additional information, such as text, audio or other metadata, that forms a
rather sparse and noisy, yet rich and diverse source of annotation,
ideally suited to emerging weakly supervised and active machine
learning technology. The ALLEGRO project will take visual recognition
to the next level by using this largely untapped source of data to
automatically learn visual models. The main research objective of
our project is the development of new algorithms and computer software
capable of autonomously exploring evolving data collections, selecting
the relevant information, and determining the visual models most
appropriate for different object, scene, and activity categories. An
emphasis will be put on learning visual models from video, a
particularly rich source of information, and on the representation of
human activities, one of today's most challenging problems in computer
vision. Although this project addresses fundamental research
issues, it is expected to result in significant advances in
high-impact applications that range from visual mining of the Web and
automated annotation and organization of family photo and video albums
to large-scale information retrieval in television archives.
Fields of science
Call for proposal
ERC-2012-ADG_20120216
See other projects for this call
Funding Scheme
ERC-AG - ERC Advanced GrantHost institution
78153 Le Chesnay Cedex
France