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 excited to talk to researches from other disciplines, such as computational biology, medicine, astronomy, or environmental physics - and see how we can help them. On the methodological side we focus on deep learning, explainable machine learning, scalable optimization of graphical models, as well as training data simulation.
- We are happy to be involved in the organization of the Robust Vision Challenge at CVPR 2018 together with Andreas …
- Ever wondered how to train a computer vision pipeline, which contains RANSAC, in an end-to-end fashion? See our project page …
- Best Science Paper Award at BMVC 16! For our joint work with Gene Myers team about Mapping Random Forests to …
- Bogdan Savchynskyy got a 3-year DFG Grant accepted on the topic of exact and efficient inference in challenging Random Fields …
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.