Research Grant
[Cite as https://purl.org/au-research/grants/arc/LP220200949]
Researchers:
Ezequiel Marzinelli
(Chief Investigator)
,
Marzinelli, Ezequiel
(Chief Investigator)
,
Stefan Williams
(Chief Investigator)
,
Tongliang Liu
(Chief Investigator)
,
Dr Adrian Flynn
(Partner Investigator)
View all 7 related researchers
Brief description Self-supervised feature learning for rapid processing of marine imagery. Fast and reliable quantitative estimates of marine environmental health are needed for scientific studies, design and management of protected areas, and regulatory compliance of industrial activity in the ocean. Australia is collecting seafloor images at increasing rates but expert annotations are not keeping up, meaning that typical machine learning approaches struggle. This project will develop self-supervised techniques that use large amounts of unlabeled data to enhance performance. Our design takes advantage of additional information available for marine imagery such as geolocation and remote sensing context. We will explore how these representations can guide additional sampling and improve performance in classification tasks.
Funding Amount $478,994
Funding Scheme Linkage Projects
- PURL : https://purl.org/au-research/grants/arc/LP220200949
- ARC : LP220200949