Prof. Dr. Björn Ommer will give the keynote talk at the Computer Vision Winter Workshop 2025!

Title: Translating Diffusion Image Models to Other Modalities

Abstract: Recently, generative models for learning image representations have seen unprecedented progress. Approaches such as diffusion models and transformers have been widely adopted for various tasks related to visual synthesis, modification, analysis, retrieval, and beyond. Despite their enormous potential, current generative approaches have their own specific limitations. We will discuss how recently popular strategies such as flow matching can significantly enhance efficiency and democratize AI by empowering smaller models.
The main part of the talk will then investigate effective ways to utilize pretrained diffusion-based image synthesis models for different tasks and modalities. Therefore, we will efficiently translate powerful generative image representations to different modalities and show evaluations on other tasks.

Prof. Dr. Björn Ommer is a full professor at the Ludwig Maximilian University of Munich (LMU) where he heads the Computer Vision & Learning (CompVis) Group (formerly the Computer Vision Group, Heidelberg University). Before he was a full professor at the Department of Mathematics and Computer Science of Heidelberg University and also served as a one of the directors of the Interdisciplinary Center for Scientific Computing (IWR) and of the Heidelberg Collaboratory for Image Processing (HCI). The CompVis research group conducts fundamental research in Computer Vision and Machine Learning and has been exploring their applications in areas as diverse as the Digital Humanities and the Life Sciences.

Björn Ommer has studied computer science together with physics as a minor subject at the University of Bonn. Afterwards he pursued his doctoral studies in computer science at ETH Zurich. He received his Ph.D. degree from ETH Zurich for his dissertation “Learning the Compositional Nature of Objects for Visual Recognition” which was awarded the ETH Medal. Thereafter, he held a post-doc position in the Computer Vision Group of Jitendra Malik at UC Berkeley.

He serves in the Bavarian AI Council, as an associate editor for the journal IEEE T-PAMI, and previously for Pattern Recognition Letters. Björn is an ELLIS Fellow, an ELLIS unit faculty of the ELLIS unit Munich, affiliated with the Helmholtz foundation, and a PI of the Munich Center for Machine Learning (MCML). He has served as program chair for GCPR, as Senior Area Chair and Area Chair for multiple CVPR, ICCV, ECCV, and NeurIPS conferences, and as workshop and tutorial organizer at these venues. Björn delivered the opening keynote at NeurIPS’23, was awarded the German AI Prize 2024, and the work leading to Stable Diffusion has been nominated for the German Future Prize of the President of Germany.