Project description
Data efficiency in training vision and language models
Artificial intelligence (AI) is important in healthcare, medical diagnostics, and cultural heritage. Recent advances in foundation models, including vision-language models (VLMs), have demonstrated capabilities, but their training requires computational resources, increasing environmental costs and limiting accessibility. DeTAI addresses this challenge by investigating whether similar performance can be achieved with significantly less data. Supported by the Marie Skłodowska-Curie Actions programme, the DeTAI project aims to reduce VLM training data requirements by half while maintaining performance through analysing data redundancy, improving multimodal dataset distillation methods, and benchmarking data-efficient training strategies for generalisation, robustness, and fairness, particularly in low-data applications such as medical imaging. DeTAI will support responsible foundation model research, especially for under-resourced communities.
Objective
Artificial intelligence (AI) has become integral to modern life, with applications ranging from healthcare, medical diagnostics to cultural heritage preservation. Recent progress in foundation models, notably vision-language models (VLMs) and multimodal large language models, has demonstrated remarkable capabilities across diverse tasks. Yet, their training and deployment demand enormous computational resources, creating significant environmental costs and limiting accessibility. Current research emphasises scaling models and datasets to boost performance, but comparatively little attention is given to the usage efficiency and quality of the underlying data. A key question arises: must we always rely on all available data, or can similar performance be achieved with substantially less data, thereby reducing costs and environmental impact?
This proposal, DeTAI, addresses this challenge by improving data efficiency in VLM training. The project targets a 50% reduction in data volume while maintaining performance within 2% of standard benchmarks. DeTAI introduces a unified framework for analysing data redundancy through insights into VLM learning dynamics, enabling principled selection and generation of high-quality data. Redundancy quantification will inform the design of multimodal dataset distillation methods that exploit model learning patterns. The project will conduct comprehensive benchmarking of data-efficient training strategies, including evaluations of generalisation, robustness and fairness in low-data regimes such as medical image analysis.
By advancing state of the art in data-efficient VLM training, DeTAI promotes sustainable AI development and reliable deployment. It democratises access to foundation model research, particularly for under-resourced groups. Beyond its scientific contributions, DeTAI will strengthen the fellow’s expertise in AI and project leadership, consolidating the position as an emerging leader in efficient and responsible AI.
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: The European Science Vocabulary.
This project's classification has been human-validated.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
- natural sciences computer and information sciences artificial intelligence machine learning unsupervised learning
- natural sciences computer and information sciences artificial intelligence computer vision
- natural sciences computer and information sciences artificial intelligence frugal artificial intelligence
- natural sciences computer and information sciences artificial intelligence generative artificial intelligence
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
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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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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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) HORIZON-MSCA-2025-PF
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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.
BA2 7AY BATH
United Kingdom
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.