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Field of Research : Computer Vision
Australian State/Territory : ACT
Scheme : ARC Future Fellowships
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  • Active Funded Activity

    ARC Future Fellowships - Grant ID: FT200100421

    Funder
    Australian Research Council
    Funding Amount
    $1,048,712.00
    Summary
    Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory a .... Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory and algorithms that allow physical and mathematical models to be embedded within a deep learning framework, providing performance guarantees and interpretability. This would likely benefit machine learning based products that can understand the world and interact with humans naturally through vision and language.
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    Funded Activity

    ARC Future Fellowships - Grant ID: FT0991969

    Funder
    Australian Research Council
    Funding Amount
    $686,400.00
    Summary
    Advanced Interface Technologies for Computational Science & Simulation. The project will research novel computer vision technologies that enable the next generation of visualisation portals for scientific collaboration. The development of new computer vision tools is key to truly natural human-machine interaction. The research outcomes of this project directly align with National Research Priority 3: Frontier Technologies. It supports four of the five relevant priority goals - Breakthrough Scien .... Advanced Interface Technologies for Computational Science & Simulation. The project will research novel computer vision technologies that enable the next generation of visualisation portals for scientific collaboration. The development of new computer vision tools is key to truly natural human-machine interaction. The research outcomes of this project directly align with National Research Priority 3: Frontier Technologies. It supports four of the five relevant priority goals - Breakthrough Science, Frontier Technologies, Smart Information Use, and Promoting an Innovation Culture and Economy. Outcomes of this research are also relevant to Research Priority 4: Safeguarding Australia, and has direct applications to video surveillance technology. Significant commercial opportunities, including licensing and spin-offs exist.
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    Funded Activity

    ARC Future Fellowships - Grant ID: FT0991771

    Funder
    Australian Research Council
    Funding Amount
    $788,800.00
    Summary
    Foundations of Vision Based Control of Robotic Vehicles. Automated and partially automated robotic vehicles are an emerging technology in society. The safety and performance of such systems depends crucially on their sensing and control algorithms. Vision sensing is one of the few sensor modalities that has the potential to adequately represent the complexity of a real world environment. By providing simple and effective vision based control algorithms this project develops Frontier Technologi .... Foundations of Vision Based Control of Robotic Vehicles. Automated and partially automated robotic vehicles are an emerging technology in society. The safety and performance of such systems depends crucially on their sensing and control algorithms. Vision sensing is one of the few sensor modalities that has the potential to adequately represent the complexity of a real world environment. By providing simple and effective vision based control algorithms this project develops Frontier Technologies for Building and Transforming Australian Industries by enabling a wide range of robotic vehicle applications, including aerial, submersible, and wheeled vehicles.
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