CORDIS Project
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This project focuses on creating a framework for transfer learning with 3D geometric data, allowing for more efficient analysis across different tasks and data types. By leveraging pre-trained models and minimal data, it aims to improve the adaptability of learning methods in various applications, including biological…
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 fo…
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