| KEYWORD |
DAUIN - GR-02 - COMPUTER GRAPHIC AND VISION GROUP - CGVG
Using generative AI to create annotated datasets for wet damage identification
keywords GENERATIVE AI, ANNOTATED DATASETS, INSTANCE SEGMEN
Reference persons ANDREA BOTTINO
Research Groups DAUIN - GR-02 - COMPUTER GRAPHIC AND VISION GROUP - CGVG
Thesis type RESEARCH / EXPERIMENTAL
Description This work focuses on the automatic detection of wet damages from images. A major challenge in this work is the lack of annotated datasets large enough to effectively train instance segmentation algorithms. The core idea of this thesis proposal is to use the capabilities of generative AI to create a synthetic dataset of annotated images. Using a small set of existing annotated images, the work aims to develop a generative AI approach (GenAI) that can extrapolate and generate a comprehensive dataset that simulates different scenarios and conditions of wet damage. This dataset will then be used to train robust instance segmentation algorithms to improve their accuracy and effectiveness in real-world applications.
The expected outcome of this work is twofold. First, to successfully demonstrate the feasibility of using generative AI to create large, diverse and reliable annotated datasets from a minimal number of real annotated images. Second, to evaluate the performance of instance segmentation algorithms trained on these synthetic datasets.
Possibility of expense reimbursement (with a research grant).
See also https://areeweb.polito.it/ricerca/cgvg/thesis.html
Required skills Machine Learning, Deep Learning, Python
Deadline 15/01/2025
PROPONI LA TUA CANDIDATURA