Objective
Audiovisual media content created and used in films and videos is key for people to communicate and entertain. It has also become an essential resource of modern history, since a large portion of memories and records of the 20th and 21st centuries are audiovisual. To fully benefit from this asset, fast and effective methods are needed to cope with the rapidly growing audiovisual big data that are collected in digital repositories and used internationally. MeMAD will provide novel methods for an efficient re-use and re-purpose of multilingual audiovisual content which revolutionize video management and digital storytelling in broadcasting and media production. We go far beyond the state-of-the-art automatic video description methods by making the machine learn from the human. The resulting description is thus not only a time-aligned semantic extraction of objects but makes use of the audio and recognizes action sequences. While current methods work mainly for English, MeMAD will handle multilingual source material and produce multilingual descriptions and thus enhance the user experience. Our method interactively integrates the latest research achievements in deep neural network techniques in computer vision with knowledge bases, human and machine translation in a continuously improving machine learning framework. This results in detailed, rich descriptions of the moving images, speech, and audio, which enable people working in the Creative Industries to access and use audiovisual information in more effective ways. Moreover,the intermodal translation from images and sounds into words will attract millions of new users to audiovisual media, including the visually and hearing impaired. Anyone using audiovisual content will also benefit from these verbalisations as they are non-invasive surrogates for visual and auditory information, which can be processed without the need of actually watching or listening, matching the new usage of video consumption on mobile devices.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- humanities history and archaeology history modern history
- natural sciences computer and information sciences data science big data
- natural sciences computer and information sciences artificial intelligence computer vision
- natural sciences computer and information sciences knowledge engineering
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
RIA - Research and Innovation action
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-ICT-2016-2017
See all projects funded under this callCoordinator
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
02150 Espoo
Finland
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.