5,352 research outputs found
CHORUS Deliverable 3.3: Vision Document - Intermediate version
The goal of the CHORUS vision document is to create a high level vision on audio-visual search engines in order to give guidance to the future R&D work in this area (in line with the mandate of CHORUS as a Coordination Action).
This current intermediate draft of the CHORUS vision document (D3.3) is based on the previous CHORUS vision documents D3.1 to D3.2 and on the results of the six CHORUS Think-Tank meetings held in March, September and November 2007 as well as in April, July and October 2008, and on the feedback from other CHORUS events.
The outcome of the six Think-Thank meetings will not just be to the benefit of the participants which are stakeholders and experts from academia and industry – CHORUS, as a coordination action of the EC, will feed back the findings (see Summary) to the projects under its purview and, via its website, to the whole community working in the domain of AV content search.
A few subjections of this deliverable are to be completed after the eights (and presumably last) Think-Tank meeting in spring 2009
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
For many applications the collection of labeled data is expensive laborious.
Exploitation of unlabeled data during training is thus a long pursued objective
of machine learning. Self-supervised learning addresses this by positing an
auxiliary task (different, but related to the supervised task) for which data
is abundantly available. In this paper, we show how ranking can be used as a
proxy task for some regression problems. As another contribution, we propose an
efficient backpropagation technique for Siamese networks which prevents the
redundant computation introduced by the multi-branch network architecture. We
apply our framework to two regression problems: Image Quality Assessment (IQA)
and Crowd Counting. For both we show how to automatically generate ranked image
sets from unlabeled data. Our results show that networks trained to regress to
the ground truth targets for labeled data and to simultaneously learn to rank
unlabeled data obtain significantly better, state-of-the-art results for both
IQA and crowd counting. In addition, we show that measuring network uncertainty
on the self-supervised proxy task is a good measure of informativeness of
unlabeled data. This can be used to drive an algorithm for active learning and
we show that this reduces labeling effort by up to 50%.Comment: Accepted at TPAMI. (Keywords: Learning from rankings, image quality
assessment, crowd counting, active learning). arXiv admin note: text overlap
with arXiv:1803.0309
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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