The 10 projects funded by the 1st Open Call of AI4Media’s Open Call are underway, having held their official kick-off meeting on 2 March 2022. In the context of the funding programme, AI4Media will financially support each project with €50,000 and will provide tailored coaching, market-driven services, and business support, in addition to large-scale visibility. Some of the topics addressed by the projects include AI music and audio, media authentication, fact-checking, disinformation, and much more.
The objective of the AI4Media – Open Call #1 was to engage companies and researchers to develop new research and applications for AI and contribute to the enrichment of the pool of technological tools. Submissions were required to address one of seven specific challenges or open challenges from a Research or Application track.
The 10 projects were selected from a total of 60 submissions from 22 countries. The competitive open call ran from 1 September to 1 December 2021. Eligible submissions were subject to an external evaluation by independent experts and a selected group of proposals went on to the interview stage. Each project has been awarded up to €50.000 to implement its work plan.
A quick glance at the funded projects:
AIEDJ – AI Empathic DJ App (musicube GmbH, Germany): Aims to expand on existing AI software for audio and music and adapt it to each listener’s perspective on music so that the AI learns and adapts to different musical tastes.
CIMA – Next-Gen Collaborative Intelligence for Media Authentication (AdVerif.ai, Israel): Aims to develop a next-generation intelligence platform to make a collaborative collection of evidence for media authentication easier and faster. The platform will adopt cutting-edge AI methods from cyber-security to the media domain, empowering fact-checkers and journalists to be more effective.
CUHE – An explainable recommender system for holistic exploration and CUration of media HEritage collections (IN2 Digital Innovations GmbH, Germany): Aims to develop and demonstrate a web-based application based on AI recommendations that will allow cultural heritage professionals as well as (humanities) researchers to explore existing media and cultural heritage digital collections in a more holistic way and allow them to curate new galleries or create digital stories and exhibitions which can showcase and share the new insights gained.
InPreVI – Inauthentic web traffic Prediction in Video marketing campaigns for investment optimization (JOT Internet Media, Spain): Aims to develop an innovative AI-based system, using the existing JOT-owned video web traffic data to (1) identify the main behavioural patterns of inauthentic users to predict their actions and limit their impact in the video marketing campaigns and (2) model the quality score associated to a campaign.
VRES – Varia Research (Varia UG, Germany): Aims to bring AI power to the frontlines of the media industry, to the journalists. While journalistic research processes today are highly fragmented and based on workarounds, Varia Research will be the first holistic application that gives all central research activities a common home.
edgeAI4UAV – Computer Vision and AI Algorithms Edge Computation on UAVs (lnternational Hellenic University, Greece): Aims to develop an edge computation node for UAVs equipped with lightweight active computer vision and AI (deep learning) algorithms capable of detecting and tracking moving objects, while at the same time will ensure robust UAV localization and reactive navigation behaviour.
NeurAdapt – Development of a Bio-inspired, resource efficient design approach for designing Deep Learning models (Irida Labs, Greece): Aims to deliver a framework, where established techniques such as channel gating, channel attention and calibrated dropout, are synthesized in order to formulate a building block of and novel methodology for designing CNN models.
RobaCOFI – Robust and adaptable comment filtering (Institut Jozef Stefan, Slovenia): Aims to develop new methods to bypass the problem of filtering and moderating comments and make the initial implementation process easy and fast; develop methods for semi-automatic annotation of data, including new variants of active learning in which the AI tools can quickly select the data they need to be labelled.
SMAITE – Preventing the Spread of Misinformation with AI-generated Text Explanations (University of Manchester, United Kingdom): Aims to develop a fact-checking system underpinned by deep learning-based, generative language models that will generate explanations that meet the identified requirements.
TRACES – AuTomatic Recognition of humAn-written and deepfake-generated text disinformation in soCial mEdia for a low-reSourced language (Sofia University “St. Kliment Ohridski”, GATE Institute, Bulgaria): Aims to provide solutions to the problem of fake content and disinformation spread worldwide and across Europe, and the detection of deep fakes, by creating methods and resources for detecting both human and deepfake disinformation in social media for low-resourced languages.
More information about the projects HERE
Authors: Samuel Almeida & Catarina Reis, (F6S)