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Research Topic : Neural coding
Scheme : Discovery Projects
Field of Research : Computer Vision
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP210100640

    Funder
    Australian Research Council
    Funding Amount
    $425,912.00
    Summary
    A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac .... A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.
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    Funded Activity

    Discovery Projects - Grant ID: DP0987421

    Funder
    Australian Research Council
    Funding Amount
    $245,000.00
    Summary
    Automatic Human Age Estimation Based on Visual Information. Age verification is important for many security applications including passport control for border security, and protecting children from adult websites, venues, or products. Accurate, reliable and practical age estimation or verification technologies would be of enormous benefit for 'Safeguarding Australia'. The ability of a machine to estimate a person's age and provide an age-appropriate interface also has benefits for the young and .... Automatic Human Age Estimation Based on Visual Information. Age verification is important for many security applications including passport control for border security, and protecting children from adult websites, venues, or products. Accurate, reliable and practical age estimation or verification technologies would be of enormous benefit for 'Safeguarding Australia'. The ability of a machine to estimate a person's age and provide an age-appropriate interface also has benefits for the young and old in our society. The outcome of this project, practical technologies for automatic human age estimation based on visual information, will dramatically change the current (non-technology based) methods of age verification and create new opportunities for customised human-machine interfaces.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP210102801

    Funder
    Australian Research Council
    Funding Amount
    $506,671.00
    Summary
    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.
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    Funded Activity

    Discovery Projects - Grant ID: DP0209969

    Funder
    Australian Research Council
    Funding Amount
    $370,808.00
    Summary
    Concept-based retrieval and interpretation for large data sets. Access to on-line information is growing at an exponential rate, fuelled by advances in computing and communications technologies. Current information retrieval methods are becoming ineffective due to their reliance on simple term-based methods, resulting in a massive number of matches, of which only a small proportion are relevant. We address this problem by developing new matching algorithms which understand the underlying .... Concept-based retrieval and interpretation for large data sets. Access to on-line information is growing at an exponential rate, fuelled by advances in computing and communications technologies. Current information retrieval methods are becoming ineffective due to their reliance on simple term-based methods, resulting in a massive number of matches, of which only a small proportion are relevant. We address this problem by developing new matching algorithms which understand the underlying meaning of documents in database repositories - by building semantic structures semi-automatically - and thus provide more relevant information to queries. This project will be of great benefit to a multitude of end-users in medicine, history, law and many other disciplines.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220100800

    Funder
    Australian Research Council
    Funding Amount
    $350,000.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200100938

    Funder
    Australian Research Council
    Funding Amount
    $486,000.00
    Summary
    Automatic Machine Learning with Imperfect Data for Video Analysis . This project aims to propose new algorithms and technologies for constructing an efficient video analysis system, which will be aligned with Australia’s science and research priorities. Specifically, during this project, a novel network structure search method based on auto machine learning will be proposed, an unsupervised domain adaptation algorithm will be developed, and a generative data augmentation method will be construct .... Automatic Machine Learning with Imperfect Data for Video Analysis . This project aims to propose new algorithms and technologies for constructing an efficient video analysis system, which will be aligned with Australia’s science and research priorities. Specifically, during this project, a novel network structure search method based on auto machine learning will be proposed, an unsupervised domain adaptation algorithm will be developed, and a generative data augmentation method will be constructed. All of these will construct a stable and efficient deep neural network, which is able to process large size videos captured from real scenarios in high efficiencies. Various fields, such as health care service and cybersecurity, will benefit hugely from this project.
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    Showing 1-6 of 6 Funded Activites

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