Björn Ommer


University of Munich, Germany

Boosting Diffusion Models for Visual Synthesis

Bio:Björn Ommer is a full professor at Ludwig Maximilian University of Munich where he is heading the Computer Vision & Learning Group. Before, he was a full professor in the department of mathematics and computer science at Heidelberg University and a co-director of its Interdisciplinary Center for Scientific Computing. He received his diploma in computer science from University of Bonn, his PhD from ETH Zurich, and he was a postdoc at UC Berkeley. Björn serves in the Bavarian AI council and has been an associate editor for IEEE T-PAMI. His research interests include semantic scene understanding and retrieval, generative AI and visual synthesis, self-supervised metric and representation learning, and explainable AI. Moreover, he is applying this basic research in interdisciplinary projects within neuroscience and the digital humanities. His group has published a series of generative approaches, including work known as "VQGAN" and "Stable Diffusion", which are now democratizing the creation of visual content and have already opened up an abundance of new directions in research, industry, the media, and beyond.

Abstract: Coming soon...