Audiovisual media content is an essential resource of modern history. It’s our way to communicate and to entertain. To fully benefit from multilingual audiovisual content, we need efficient tools to make visual content accessible in words.
We provide new methods that help to translate moving images and sounds into words. MeMAD methods will help us to manage large amounts of audiovisual data cost efficiently.
Anyone using audiovisual content will benefit from MeMAD. Professionals who work in the Creative Industries will receive new methods for video management and digital storytelling. Visually and hearing impaired, among others, will have better access to video content.
MeMAD is an EU funded H2020 research project (2018-2020). MeMAD will develop methods for an efficient re-use and re-purpose of multilingual audiovisual content targeting to 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.”
MeMAD’s main results are detailed, rich descriptions of the moving images, speech, and audio. We integrate 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.
Yle, the Finnish Broadcasting Company, has published a blog post about the experiments our project has done with the machine-translated subtitles. Four video examples used in our consumer evaluations are…
The beginning of 2021 will bring a lot of interesting activities to tune in online! The MeMAD project will host three webinars to present the final results and findings of…
Earlier this year, we published some early findings from focus groups where viewers discussed their reactions to machine-translated subtitles. In the past few months, we have conducted another two focus groups…
End-to-end named entity recognition for spoken Finnish Named entity recognition (NER) is a natural language processing task in which the system tries to find named entities and classify them in…
MeMAD project partners Lingsoft and University of Helsinki have received funding in the first call for pilot projects of the European Language Grid (ELG). The project funding stage was fairly competitive:…
MeMAD project partners have received recognition for their accomplishments. Three project innovations were recognized by the European Commission’s Innovation Radar. Innovation Radar is a European Commission initiative that champions potential…
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