Computer Vision and Machine Learning

The Machine Learning and Computer Vision group studies and develops algorithms in the area of statistical machine learning and computer vision, for example for the task of natural image understanding. Using tools from probability theory, optimization and linear algebra, the members of the group study questions such as: How can a system learn continuously over a long time in a changing environment? and How can a computer identify which objects are visible in an image?

Christoph Lampert
Institute of Science and Technology Austria (IST Austria)
Am Campus 1
A – 3400 Klosterneuburg

Phone: +43 (0)2243 9000-3101


Publication list

Lampert Group website


Elisabeth Hacker
Phone: +43 (0)2243 9000-1015


  • Paul Henderson, Postdoc
  • Nikola Konstantinov, PhD student
  • Mary Phuong, PhD student
  • Amelie Royer, PhD Student

Open Positions

Please visit the group website and read the comments for applicants  before contacting me about open positions.

Current Projects

  • Lifelong learning for visual scene understanding (L3ViSU)
    Our goal in the L3ViSu project is to develop and analyse algorithms that use continuous, open-ended machine learning from visual input data (images and videos) in order to enable a computer to interpret visual scenes. The main underlying hypothesis is that we can only significantly improve the state of the art in computer vision algorithms by giving them access to background and contextual knowledge about the visual world, and that the most effective way to obtain such knowledge is by extracting it (semi-)automatically from incoming visual stimuli. Consequently, at the core of L3ViSU lies the idea of life-long visual learning, which we study both on the level of machine learning theory as well as application. See

Selected Publications

  • Lampert CH, Blaschko MB, Hofmann T. 2009. Efficient subwindow search: A branch and bound framework for object localization. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • Lampert C.H, Nickisch H, Harmeling S. 2014. Attribute-Based Classification for Zero-Shot Visual Object Categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • Lampert CH, Pentina A. 2014. A PAC-Bayesian Bound for Lifelong Learning. International Conference on Machine Learning (ICML)


2015 Professor, IST Austria
2010 Assistant Professor, IST Austria
2007–2010 Senior Research Scientist, Max-Planck Institute, Tübingen, Germany
2004–2007 Senior Researcher, German Research Center for Artificial Intelligence, Kaiserslautern, Germany
2003 PhD, University of Bonn, Germany

Selected Distinctions

2013 Elected member of the "Junge Kurie" (Young Academy) of the Austrian Acadamy of Sciences (ÖAW)
2012 ERC Starting Grant
2008 Main Price, German Society for Pattern Recognition (DAGM)
2008 Best Paper Award, IEEE Conference for Computer Vision and Pattern Recognition (CVPR)
2008 Best Student Paper Award, European Conference for Computer Vision (ECCV)

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