Discovery Early Career Researcher Award - Grant ID: DE200101283
Funder
Australian Research Council
Funding Amount
$400,998.00
Summary
Data synthesis to quantitatively understand and improve vision systems. This project aims to build high-fidelity synthetic data, to understand how a machine vision system reacts to environmental factors and consequently improve the ability of the system to generalise in the real world. This project expects to generate new knowledge in the area of computer vision using innovative techniques of data synthesis, analysis, and domain adaptation. The expected outcomes include new scientific discoverie ....Data synthesis to quantitatively understand and improve vision systems. This project aims to build high-fidelity synthetic data, to understand how a machine vision system reacts to environmental factors and consequently improve the ability of the system to generalise in the real world. This project expects to generate new knowledge in the area of computer vision using innovative techniques of data synthesis, analysis, and domain adaptation. The expected outcomes include new scientific discoveries and domain adaptation algorithms derived from synthetic data for real-world applications. The benefits are expected to be widespread across sectors such as transportation, security, and manufacturing, including safer robotic navigation, defect detection, and smart video surveillance to improve community safety.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101390
Funder
Australian Research Council
Funding Amount
$402,900.00
Summary
Towards Human-like Machine Perception for Embodied AI. This project aims to investigate human-like visual perception, whereby AI machines can see and interpret the world like a human. The expected outputs will empower AI machines with the abilities of human-centered visual recognition and annotation-efficient learning through a set of deep learning techniques, and the ability to actively gather visual information through a reinforcement learning methodology (for decision support). This research ....Towards Human-like Machine Perception for Embodied AI. This project aims to investigate human-like visual perception, whereby AI machines can see and interpret the world like a human. The expected outputs will empower AI machines with the abilities of human-centered visual recognition and annotation-efficient learning through a set of deep learning techniques, and the ability to actively gather visual information through a reinforcement learning methodology (for decision support). This research is fundamental to the creation of embodied AI machines, which are expected to provide assistance to humans in industry, education and health. It thus will indicate immediate applications embracing autonomous vehicles and domestic robotics, providing scientific, social and economic benefits for Australia.Read moreRead less
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.Read moreRead less
Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collabor ....Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collaborations with machine learning and medical image analysis research institutions. This should provide significant benefits, such as better understanding of deep learning theory, new deep learning applications that can use previously unexplored data sets, and training for the future Australian workforce.Read moreRead less
Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to vi ....Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to video surveillance applications, which can enhance Australia’s homeland security.Read moreRead less
Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcom ....Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcomes include the ability to ingest multiple video feeds into a dense and dynamic 3D reconstruction for knowledge representation and discovery, and analysis of events and behaviour through new spatio-temporal analytic approaches. This will offer significant benefits for video forensic analysis, policing, and emergency response.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL170100006
Funder
Australian Research Council
Funding Amount
$3,016,065.00
Summary
Pattern analysis for accelerating scientific innovation. This project aims to determine how pattern recognition can be harnessed to accelerate and expand the capability of experimental optimisation that underpins scientific innovation. Disrupting current experimental methods, this new framework will use data-driven models to guide humans through experimental complexity. The expected outcomes of the project include advancing the theory and practice of pattern recognition in Bayesian optimisation ....Pattern analysis for accelerating scientific innovation. This project aims to determine how pattern recognition can be harnessed to accelerate and expand the capability of experimental optimisation that underpins scientific innovation. Disrupting current experimental methods, this new framework will use data-driven models to guide humans through experimental complexity. The expected outcomes of the project include advancing the theory and practice of pattern recognition in Bayesian optimisation by solving both fundamental and translatory problems, totally transforming the way complex experimental explorations can be done. The project will establish Australia as a leader in innovation-led productivity in the 4th industrial revolution, which will include ground-breaking investigations into the use of pattern recognition to navigate complexity in the experimental process.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL170100117
Funder
Australian Research Council
Funding Amount
$3,208,192.00
Summary
On snapping up semantics of dynamic pixels from moving cameras. The project aims to develop a suite of original models and algorithms for processing and understanding videos captured by moving cameras, and to establish the mathematical foundations for deep learning-based computer vision to provide theoretical underpinnings. The project expects to generate new knowledge that will transform moving-camera computer vision with step-changes in visual quality enhancement, compression and acceleration ....On snapping up semantics of dynamic pixels from moving cameras. The project aims to develop a suite of original models and algorithms for processing and understanding videos captured by moving cameras, and to establish the mathematical foundations for deep learning-based computer vision to provide theoretical underpinnings. The project expects to generate new knowledge that will transform moving-camera computer vision with step-changes in visual quality enhancement, compression and acceleration technologies, and solutions for fundamental computer vision tasks. A new concept of feature complexity for measuring the discriminant and learnable abilities of features from deep models will also be defined. The outcomes of the project will be critical for enabling autonomous machines to perceive and interact with the environment.Read moreRead less
Assistive micro-navigation for vision impaired people. This project aims to develop novel algorithms to transform a simple camera into a smart sensor, that can enable a vision-impaired person to navigate freely and without additional aids in a crowded area. Such a smart sensor will be endowed with the capability to detect and locate obstacles, identify the walking path, recognise objects and traffic signs and convey step-by-step instructions to the user. The project outcomes are expected to impr ....Assistive micro-navigation for vision impaired people. This project aims to develop novel algorithms to transform a simple camera into a smart sensor, that can enable a vision-impaired person to navigate freely and without additional aids in a crowded area. Such a smart sensor will be endowed with the capability to detect and locate obstacles, identify the walking path, recognise objects and traffic signs and convey step-by-step instructions to the user. The project outcomes are expected to improve the well-being and accessibility to public areas for vision-impaired people and reduce physical access disparities for this disadvantaged and vulnerable group. Furthermore, technologies developed in this project can potentially be adapted for use in related special navigation applications such as road safety, self-driving vehicles, and autonomous robots.Read moreRead less
Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imagin ....Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imaging scenarios with high heterogeneity and limited training data. The frameworks will facilitate high-throughput processing for a wide range of microscopy image modalities and biological applications, and potentially become the next generation computational platform to support fundamental research in human biology.Read moreRead less