Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global com ....Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global competitiveness. Expected outcomes of this project will help to break the communication barriers between the Deaf and hearing population. This should provide significant benefits to Deaf communities through enhanced communication and improved quality-of-life, leading to a fair, more inclusive and resilient Australian society.Read moreRead less
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
Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capabili ....Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capability and competitiveness in AI, and deliver robust and trustworthy learning technology. The project should provide significant benefits not only in advancing scientific and translational knowledge but also in accelerating AI innovations, safeguarding cyberspace, and reducing the burden on defence expenses in Australia.Read moreRead less
Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance witho ....Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance without accessing expensive test labels and improvements to system generalisation. This should provide significant benefits for computer vision applications that currently require expensive labelling, and commercial and economic benefits across sectors such as transportation, security and manufacturing.Read moreRead less
Semantic Vectorisation: From Bitmaps to Intelligent Representations. The objective of this innovative project is to provide a solution to the open question of representing natural images by semantically rich vector graphics. The challenges are to identify key visual and temporal elements for images and videos, and efficiently decompose the visual data into semantic vector representations that are faithful to original data, compact and editable. The project aims to investigate new bitmap-to-vecto ....Semantic Vectorisation: From Bitmaps to Intelligent Representations. The objective of this innovative project is to provide a solution to the open question of representing natural images by semantically rich vector graphics. The challenges are to identify key visual and temporal elements for images and videos, and efficiently decompose the visual data into semantic vector representations that are faithful to original data, compact and editable. The project aims to investigate new bitmap-to-vector conversion methods. It is expected to develop a framework where semantic labels and hyperlinks can be embedded in visual data automatically. It hopes to pioneer the creation of a web of images where the links are on image/video regions. New image simplification, stylisation, and non-photorealistic rendering methods are expected to be provided.Read moreRead less
Expectations and commitments in the Australia-USA alliance. This project aims to investigate the gap between the high expectations of mutual support and the lack of detailed security commitments in the Australia-US Alliance. The project intends to use a focused approach that captures thematic aspects of the alliance through project frames and historical slices across time. Expected outcomes can advance understanding of how alliances operate as security institutions. The outcomes can help to prom ....Expectations and commitments in the Australia-USA alliance. This project aims to investigate the gap between the high expectations of mutual support and the lack of detailed security commitments in the Australia-US Alliance. The project intends to use a focused approach that captures thematic aspects of the alliance through project frames and historical slices across time. Expected outcomes can advance understanding of how alliances operate as security institutions. The outcomes can help to promote a more informed national conversation about the costs and benefits of Australia's security relationship with the United States of America (USA) and contribute to debates over the future of the Australia-USA Alliance during a period of strategic uncertainty.Read moreRead less
Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are propos ....Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are proposed to develop recommender systems in a more responsible manner. The result of this project will not only maintain Australia's leadership in this frontier research area, but also serve as an excellent vehicle for the education and training of Australia's next generation of scholars and engineers.Read moreRead less
Deliberative democracy in the public sphere: achieving deliberative outcomes in mass publics. This project will systematically explore ways in which citizens can engage more deeply with complex policy issues without the need to resort to massive expenditure on running multiple deliberative forums, such as citizens' assemblies. It will identify the language is needed to deliberatively inform and the vehicles for providing that information.
The impact of work-from-home environments on comfort and productivity. This project aims to quantify the effect of indoor environmental quality (IEQ) in work-from-home (WFH) settings on worker comfort, productivity and household energy use, by employing a longitudinal field monitoring approach. This project expects to generate new knowledge that will inform current indoor environment standards and regulations to make them more relevant to our “new WFH normal”. Quantifying the impact of decentral ....The impact of work-from-home environments on comfort and productivity. This project aims to quantify the effect of indoor environmental quality (IEQ) in work-from-home (WFH) settings on worker comfort, productivity and household energy use, by employing a longitudinal field monitoring approach. This project expects to generate new knowledge that will inform current indoor environment standards and regulations to make them more relevant to our “new WFH normal”. Quantifying the impact of decentralised workforces on shifting energy usage between sectors can also help in the formulation of relevant energy efficiency policies and building codes. The project will provide significant benefits such as enhancing the quality of work-life of workers and enabling better management of residential energy use.Read moreRead less
Infectious diseases, security and ethics. This project will benefit the nation directly by promoting greater understanding within the community of the national security and ethical implications of infectious disease threats; recommending policies for responding in ways that achieve better public health, national security and human rights outcomes for Australians; helping to strengthen Australia's social and economic fabric; and creating national and international linkages between academics, PhD ....Infectious diseases, security and ethics. This project will benefit the nation directly by promoting greater understanding within the community of the national security and ethical implications of infectious disease threats; recommending policies for responding in ways that achieve better public health, national security and human rights outcomes for Australians; helping to strengthen Australia's social and economic fabric; and creating national and international linkages between academics, PhD students and non-academic professionals.Read moreRead less