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Universal Geometric Transfer Learning

Project description

A new approach for 3D data

Unlocking the secrets hidden within complex 3D data poses a formidable challenge. Existing methods, often trained anew for each task, struggle by ignoring valuable shared knowledge and failing to generalise effectively. Compounded by limited training data, these approaches frequently fall short in practical applications. In this context, the ERC-funded VEGA project will develop a universal framework for transfer learning, VEGA aims to unlock the potential of pre-trainable modules and multi-scale analysis. This breakthrough promises to transcend limitations posed by scarce training data, enabling applications from tracking biological system evolution to preserving cultural heritage. VEGA’s visionary strategy heralds a new era of adaptable modelling tools, poised to revolutionise diverse fields reliant on geometric data analysis.

Objective

In this project, we propose to develop a theoretical and practical framework for transfer learning with geometric 3D data. Most existing learning-based approaches, aimed at analyzing 3D data, are based on training neural networks from scratch for each data modality and application. This means that such methods, first, ignore the wider information overlap that might exist across different tasks and object or scene categories, and, second, tend to generalize poorly beyond the specific scenarios for which they are trained. Even more fundamentally, the majority of existing techniques are limited to problem settings in which sufficient amount of training data is available, making them ill-adapted in many practical applications with limited supervision.

In this project, we suggest to take a fundamentally different approach to geometric data analysis: rather than designing independent application or class-specific solutions, we propose to develop a theoretical and practical framework for geometric transfer learning. Our main goal will be to develop universally-applicable methods by combining powerful pre-trainable modules with effective multi-scale analysis and fine-tuning, given minimal task-specific data. The overall key to our study will be analyzing rigorous ways, both theoretically and in practice, in which solutions can be transferred and adapted across problems, semantic categories and geometric data types.

Such an approach will open the door to fundamentally new tasks and modeling tools, applicable to any geometric data analysis scenario, regardless of the amount of training data available. This would allow, for example, to track the evolution of biological systems, by studying the underlying complex 3D shape dynamics, or to analyze variability in object and scene collections consisting of 3D scans of previously unseen shape categories, crucial in cultural preservation and life science applications, among myriad others.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2022-COG

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Host institution

ECOLE POLYTECHNIQUE
Net EU contribution

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.

€ 1 999 490,00
Address
ROUTE DE SACLAY
91128 Palaiseau Cedex
France

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Region
Ile-de-France Ile-de-France Essonne
Activity type
Higher or Secondary Education Establishments
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Total cost

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.

€ 1 999 490,00

Beneficiaries (1)

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