Generative Visual Pre-training on Unlabelled Big Data. This project aims to develop a generative visual pre-training of large-scale deep neural networks on unlabelled big data. Developing pre-trained visual models that are accurate, robust, and efficient for downstream tasks is a keystone of modern computer vision, but it poses challenges and knowledge gaps to existing unsupervised representation learning. Expected outcomes include new theories and algorithms for unsupervised visual pre-training ....Generative Visual Pre-training on Unlabelled Big Data. This project aims to develop a generative visual pre-training of large-scale deep neural networks on unlabelled big data. Developing pre-trained visual models that are accurate, robust, and efficient for downstream tasks is a keystone of modern computer vision, but it poses challenges and knowledge gaps to existing unsupervised representation learning. Expected outcomes include new theories and algorithms for unsupervised visual pre-training, which are anticipated to deepen our understanding of visual representation and make it easier to build and deploy computer vision applications and services. Examples of benefits include modernising machines in manufacturing and farming with visual intelligence. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101567
Funder
Australian Research Council
Funding Amount
$453,054.00
Summary
Listening to Nature: Transforming Bioacoustics through Spatial Audio. This project aims to research new 3D spatial audio processing techniques to analyse natural sounds for environmental conservation, while meeting the tasks, demands and data characteristics inherent to bioacoustics. Expected outcomes include new, accurate and efficient bioacoustics computation technologies, generalisable across different terrestrial regions, species types and environment changes. These could dramatically enhanc ....Listening to Nature: Transforming Bioacoustics through Spatial Audio. This project aims to research new 3D spatial audio processing techniques to analyse natural sounds for environmental conservation, while meeting the tasks, demands and data characteristics inherent to bioacoustics. Expected outcomes include new, accurate and efficient bioacoustics computation technologies, generalisable across different terrestrial regions, species types and environment changes. These could dramatically enhance the efficacy of current bioacoustic monitoring systems while opening up new research directions. Resulting technology could be adopted for immediate tasks like the monitoring of bushfire recovery efforts, and more generally, for the management and conservation of Australian natural resources.Read moreRead less