ARC Future Fellowships - Grant ID: FT210100268

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/FT210100268

Funding Status
Active

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Funded Activity Summary

Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.

Funded Activity Details

Start Date: 28-03-2022

End Date: 27-03-2026

Funding Scheme: ARC Future Fellowships

Funding Amount: $1,126,000.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Computer Vision | Artificial Intelligence and Image Processing

ANZSRC Socio-Economic Objective (SEO)

Road Public Transport | Information Processing Services (incl. Data Entry and Capture) | Commercial Construction Planning |

Other Keywords

Artificial Intelligence and Image Processing | Commercial Construction Planning | Computer Vision | Information Processing Services (incl. Data Entry and Capture) | Road Public Transport

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