Intelligent Virtual Human Companions. This research aims to develop intelligent virtual human companions that can seemingly integrate our immediate physical environment and understand their surroundings including people’s emotions, behaviours, actions and interactions. Such a technology will be enabled by leveraging recent advances in mixed/augmented reality technologies, and by developing innovative artificial intelligence and computer vision and graphics algorithms for dynamic real-world envir ....Intelligent Virtual Human Companions. This research aims to develop intelligent virtual human companions that can seemingly integrate our immediate physical environment and understand their surroundings including people’s emotions, behaviours, actions and interactions. Such a technology will be enabled by leveraging recent advances in mixed/augmented reality technologies, and by developing innovative artificial intelligence and computer vision and graphics algorithms for dynamic real-world environments. Unlike robots, the proposed technology will be low cost, readily deployable and customisable, and will not have any physical limitations or maintenance requirements. It will thus have a wide range of applications from elderly care, healthcare care to educational training.Read moreRead less
Tensor and Hypergraph Methods in Fitting Visual Data. This proposal will put an important class of clustering (extracting data that should fit a geometric model) on a more solid theoretical foundation. This will lead to better understanding of how to certify outcomes, efficiency, reliability etc. The type of clustering under consideration is relevant to many problems in machine learning and computer vision, as well as data mining and a wide variety of other settings.
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less