Welcome to the Visual Learning Lab Heidelberg
The Visual Learning Lab Heidelberg works at the intersection of Computer Vision and Machine Learning. This means that we take images, videos or other dense measurements and process them, or extract useful information hidden in the data. We work on a broad range of applications, such as segmentation, pose estimation, matching, tracking, detection, and scene understanding. We are interested in talking to researchers from other disciplines, such as computational biology, medicine, astronomy, or environmental physics, and see if we can contribute with our knowledge. On the methodological side we focus on deep learning, explainable machine learning, scalable optimization of graphical models, as well as training data simulation.
- Carsten Rother was selected by aminer to be among the 9 most influential Computer Vision researchers in Europe. See: AI …
- S.K. Mustikovela, V. Jampani, S. De Mello, S. Liu, U. Iqbal, C. Rother, J. Kautz “Self-Supervised Viewpoint Learning from Image …
- Ullrich Köthe, Markus Brubaeker (York University/Toronto) and Carsten Rother offer a half-day tutorial at ECCV 2020 in Glasgow on “Normalizing Flows …
- Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift [pdf] [project page]
The Rich Scene Model (ERC Consolidator Grant)
Given a sequence of images the goal is to recover a rich, detailed representation of the 3D world, ranging from physical to semantical aspects. To achieve this we investigate new ways to combine feature learning, modelling, physical laws, and optimization in large-scale discrete-continuous-valued probabilistic graphical model.
Collaborators & Industrial Partners
We have also been collaborating with various industrial research labs - such as Microsoft Research Cambridge, Bejing and Redmond, Daimler, and Facebook Artificial Intelligence Researchers (FAIR).
We are part of the Heidelberg HCI 3rd phase, where we collaborate closely with Bosch.