Computer Vision and Machine Learning

Christoph Lampert

Christoph Lampert studies and develops statistical machine learning algorithms for computer vision applications, in particular for the task of natural image understanding. Machine learning enables automatic systems to analyze large amounts of high dimensional data with significant between-feature correlations. This makes it particularly suitable for computer vision problems, such as the analysis of digital images with respect to their contents. In the long run, we are interest in building automatic systems that understand images on the same semantic level as human do, enabling them to answer questions like: What objects are visible in an image? Where are they located? How do they interact?

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

Phone: +43 (0)2243 9000-3101
E-mail: christoph.lampert@ist.ac.at

CV

Publication list

Lampert Group website

Assistant
Elisabeth Hacker

Phone: +43 (0)2243 9000-1015
E-mail: elisabeth.hacker@remove-this.ist.ac.at

Team

  • Emilie Morvant, Postdoc
  • Anastasia Pentin, PhD Student
  • Viktoriia Sharmaska, PhD student

Current Projects

  • Efficient object and action localization
    The tasks of identifying and locating objects in natural images are crucial components for any automatic image understanding system, and they are inherently linked. We work on developing fast learning and inference techniques for object localization based on formulating the problem as a regression task between structured spaces instead of a sequence of classification tasks. This allows the use of global optimization techniques, such as branch-and-bound, instead of the exhaustive search used by currently dominating sliding window approaches.
  • Attribute-enabled representations
    Image understanding on a semantic level requires knowledge not only about the presence and location of objects, but also about their properties. We work on extending existing image and object representations by attributes. These are characteristic properties of a scene or an object category that can be predicted from the image information, but also have a semantic interpretation. Our goal is to show that attribute-enabled representations can achieve higher classification accuracy than purely object-centric ones, that they require less training data and that they allow solving new image-related prediction tasks, for example for making decisions about the importance of objects in a scene.
  • Structured prediction and learning
    We are working on extending and improving existing techniques for the prediction of structured objects using machine learning techniques, in particular graph labeling problems. Treating these in a framework of empirical risk minimization allows us to develop algorithms that can learn prediction functions that are tailored to the specific prediction tasks and thereby achieve higher prediction accuracy. In particular, we work on developing techniques for handling the cases of ambiguous input and outputs.

Selected Publications

  • Lampert C.H, Nickisch H, Harmeling S. 2009. Learning to detect unseen object classes by between-class attribute transfer. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami Beach, USA.
  • Lampert CH, Blaschko MB, Hofmann T. 2008. Beyond sliding windows: Object localization by efficient subwindow search”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA.
  • Blaschko MB, Lampert CH.2008. Learning to localize objects with structured output regression, European Conference on Computer Vision, Marseilles, France.

Career

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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