Enhancing Human-Centred Visual Learning: Innovations in Vision Algorithms for Improved Human Understanding
Associate Professor, School of Computer Science, University of Birmingham
Abstract:
The progress of artificial intelligence fundamentally depends on humans, both as inventors and beneficiaries. My research on human-centred visual learning focuses on developing vision-based algorithms that prioritise the usability and usefulness of AI systems by addressing human needs and requirements based on visual cues. A crucial aspect of this work involves understanding human body pose, hand pose, eye gaze, and object interaction, as they provide valuable insights into human actions and behaviours. During this talk, I will discuss recent studies conducted by my group, with a special focus on our latest advancements in multimodal data integration. This includes audio and text-based hand-object pose and shape estimation, as well as face+eye gaze image synthesis. These innovative approaches not only enhance the accuracy and robustness of our algorithms but also open new avenues for intuitive human-AI interaction. Furthermore, I will explore the latest research trends in computer vision and demonstrate how these methodologies have been, and can be, applied to algorithm development. Additionally, we will reflect on potential future directions for this field. The applications leveraging these human-centred vision methodologies are poised to revolutionise the way AI understands and interacts with humans.
Bio:
Dr Hyung Jin Chang is an associate professor in the School of Computer Science at the University of Birmingham. He received a PhD in machine learning and computer vision at Perception and Intelligence Lab, Seoul National University, in 2013. Before joining the University of Birmingham, he was a post-doctoral researcher at Imperial College London.