Discovery Early Career Researcher Award - Grant ID: DE190100626
Funder
Australian Research Council
Funding Amount
$393,000.00
Summary
Towards data-efficient future action prediction in the wild. This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generat ....Towards data-efficient future action prediction in the wild. This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generation of intelligent systems to accommodate limited supervision. This should benefit science, society, and the economy nationally through the applications of autonomous vehicles, sensor technologies, and cybersecurity.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190101315
Funder
Australian Research Council
Funding Amount
$401,886.00
Summary
Towards extreme object detection. This project aims to provide a comprehensive and practical extreme object detection system considering three extreme object detection challenges, that is small and distance objects, occluded objects and sparse (rare) objects. Reliable extreme object detection is critical for intelligent agents in many aspects and is becoming increasingly important for developing a smart nation by building intelligent transportation and smart cities. To design and develop such an ....Towards extreme object detection. This project aims to provide a comprehensive and practical extreme object detection system considering three extreme object detection challenges, that is small and distance objects, occluded objects and sparse (rare) objects. Reliable extreme object detection is critical for intelligent agents in many aspects and is becoming increasingly important for developing a smart nation by building intelligent transportation and smart cities. To design and develop such an effective system, this project provides novel scale-invariant learning, occlusion-robust learning and semi-supervised learning solutions to address the corresponding challenges. The project is expected to have a significant impact on a broad array of application areas including autonomous vehicles, robotics, and intelligent surveillance cameras.Read moreRead less