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.
MeMAD work done on datasets and data licensing for research was presented at two conferences during late 2018. The Baltic audiovisual archives community gathered at the annual BAAC conference in…
MeMAD draft use cases were circulated for comments and feedback at the annual EBU professional workshop for metadata developers network MDN. The first day of 2018 workshop was dedicated for…
What is audio tagging? If you are a frequent social media user, then you must be familiar with the word ‘tag’. This type of tagging normally happens when you find…
During the first year of our machine translation efforts in MeMAD, we focused on participating in two shared tasks on multimodal MT: the caption translation task at WMT 2018 and…
“Wow, this is the future”, I thought to myself. “How do I get to be a part of this project”, my thoughts continued. The demo I attended in August led…
MeMAD research work – especially the parts analysing video and audio – depends on data and media because data intensive methods such as data mining are being used. MeMAD research…
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