A central question in Computer Vision is how human prior knowledge can be integrated in order to build efficient and accurate learning systems. Computer Vision and Machine Learning Researchers have come up with many different models to incorporate their prior knowledge about the domain structure - such as probabilistic graphical models, conditional random fields, or structured support vector machines - which have been applied successfully to many different areas of application. The goal of this workshop is to bring together researchers from different directions of Computer Vision and Machine Learning, and to stimulate the discussion about the shared concepts, the recent progress and the remaining problems of learning and inference algorithms for structured representations in Computer Vision.

Program Committee

  • Yasemin Altun - MPI Tuebingen
  • Francis Bach - INRIA Paris
  • Matthew Blaschko - University of Oxford
  • Tiberio S. Caetano - NICTA
  • Daniel Cremers - TU Munich
  • Leo Grady - Siemens Research
  • Stefan Harmeling - MPI Tuebingen
  • Pawan Kumar - Stanford University
  • Pushmeet Kohli - Microsoft Research Cambridge
  • Bastian Leibe - RWTH Aachen
  • Simon Lucey - CMU
  • David McAllester - TTIC
  • Sebastian Nowozin - Microsoft Research Cambridge
  • Nikos Paragios - Ecole Centrale de Paris
  • Jan Peters - MPI Tuebingen
  • Deva Ramanan - UC Irvine
  • Bodo Rosenhahn - University of Hannover
  • Carsten Rother - Microsoft Research Cambridge
  • Bernhard Schoelkopf - MPI Tuebingen
  • Josef Sivic - INRIA Paris
  • Cristian Sminchisescu - University of Bonn
  • Ben Taskar - UPenn
  • Fernando de la Torre - CMU
  • Bill Triggs - INRS Grenoble
  • Jakob Verbeek - INRIA Grenoble
  • Louis Wehenkel - University of Liege
  • Richard Zemel - University of Toronto