Visual Question Answering is one of the applications of Deep Learning that is pushing towards real Artificial Intelligence. It turns the typical deep learning process around by only defining the task to be carried out after the training has taken place, which changes the task fundamentally. We have developed a range of strategies for incorporating other information sources into deep learning-based methods, and the process taken a step towards developing algorithms which learn how to use other algorithms to solve a problem, rather than solving it directly. This talk thus covers some of the high-level questions about the types of challenges Deep Learning can be applied to, and how we might separate the things its good at from those that it’s not.
Biography: Anton van den Hengel is the Director of the Australian Centre for Visual Technologies, a Chief Investigator of the Australian Centre for Robotic Vision, and a Professor of Computer Science at the University of Adelaide. Prof. van den Hengel has published over 300 papers, been a CI on over $50m in research funding, and leads a group of over 60 researchers working in Computer Vision and Machine Learning. Prof van den Hengel has received a number of awards, including the Pearcey Award for Innovation and the CVPR Best Paper Award in 2010.