Project description
Learning semantic structure from multimodal data without human annotation
Multimodal learning involves training models using data from various modalities, such as videos containing both visual and audio components or documents featuring text and images. This technique uses pairwise data, such as image-text pairs, to train deep learning models, enabling them to learn more robust representations without the need for human supervision. The ERC-funded GraViLa project proposes that multimodal models can effectively capture cross-modal semantic entities and are particularly beneficial for analysing collections of interconnected modalities and topics, which is common in multimodal documents. The project achieves this by learning semantic structures from multimodal data through multimodal and self-supervised learning, eliminating the need for human annotation. This information is then represented as a graph, facilitating the processing and comprehension of large-scale data.
Objective
Multimodal learning focuses on training models with data in more than one modality, such as videos capturing visual and audio information or documents containing image and text. Current approaches use such data to train large-scale deep learning models without human supervision by sampling pair-wise data e.g. an image-text pair from a website and train the network e.g. to identify matching vs. not matching pairs to learn better representations.
We argue that multimodal learning can do more: by combining information from different sources, multimodal models capture cross-modal semantic entities, and as most multimodal documents are a collection of connected modalities and topics, multimodal models should allow us to capture the inherent high-level topology of such data. The goal of the following project is to learn semantic structures from multimodal data to capture long-range concepts and relations in multimodal data via multimodal and self-supervision learning without human annotation. We will represent this information in form of a graph, considering latent semantic concepts as nodes and their connectivity as edges. Based on this structure, we will extend current unimodal approaches to capture and process data from different modalities in a single structure. Finally, we will explore the challenges and opportunities of the proposed idea with respect to their impact on two main challenges in machine learning: data-efficient learning and fairness in label-free learning.
By bridging the gap between those two parallel trends, multimodal supervision and graph-based representations, we combine their strengths of generating and processing topological data, which will not only allow to build new applications and tools but also opens new ways of processing and understanding large-scale data that are out-of-reach at the moment.
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.
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.
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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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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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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-ERC - HORIZON ERC Grants
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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) ERC-2023-STG
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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.
72074 Tuebingen
Germany
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.