Human centric visual analysis tasks are essential to computer vision since humans are the key element for cameras to analyze. In this talk, I will…

Graph neural networks (GNNs) have achieved impressive results in various graph learning tasks and they have found their way into many application…

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a…

Recent advances in multimodal processing have led to promising solutions for speech-processing tasks. One example is automatic speech recognition …

Brain data are inherently large scale, multidimensional, and noisy. Indeed, advances in imaging and sensor technology allow recordings of ever…

In the cognitive neurosciences and machine learning, we have formal ways of understanding and characterising perception and decision-making; however,…

While message-passing neural networks (MPNNs) are the most popular architectures for graph learning, their expressive power is inherently limited. In…

Focus stacking is an effective approach to extending the depth of field of a camera, yet is challenging with regard to 1) controlling focal planes in…

Many tasks in medicine still involve substantial manual work. In many cases there is strong potential for intelligent automation by Artificial…

Non-line-of-sight (NLoS) imaging is an exciting technology which uses time-resolved sensors to see objects that are hidden from view. In essence,…