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ECML/PKDD 2014 Workshop on Learning with Multiple Views: Applications to Computer Vision and Multimedia Mining

16 Avril 2014

Catégorie : Conférence internationale

The workshop on Learning with Multiple Views: Applications to Computer Vision and Multimedia Mining will take place as a satelite workshop of the ECML/PKDD 2014 conference at Nancy (France).

For further information and future updates please visit

Please notice the workshop will held either on September 15th or 19th.



Recent years have witnessed new frameworks/algorithms able to deal with multiple views, such as Multiple Kernel Learning (MKL), Boosting, Co-regularized approach. Such algorithms come from the Machine Learning community and find applications in many different areas, such as Multimedia Indexing, Computer Vision, Bio-informatics, Neuro-imaging... Multiview learning, naturally enough, emphasise the potential benefits of learning through collaboration with multiple sources of data (e.g. video document can be described through images, sound, motion, text). Depending on the context, this issue of learning from multiple descriptions of data goes under the name of multiview learning (machine learning, computer vision), multimodality fusion (multimedia), among others. This workshop is the opportunity to bring together theoretical and applicative communities around multiview learning, which could lead to significant contributions in Machine Learning, Multimedia Mining and Computer Vision.

This workshop builds upon successful previous machine learning workshops on multiview learning or connections between ML and applications, like Machine Learning techniques for processing multimedia content (ICML 2005), Learning with multiples views (ICML 2005), Learning from multiples sources (NIPS 2008),Learning from multiples sources with applications to robotics (NIPS 2009) where links between theory and applications of the multiview paradigms are made. The literature and the advances on multiview learning have grown up to a point where a broad synthesis is required.


The main objectives of this workshop are to 1) introduce recent development in machine learning with multiview setting, 2) focus on various problematics in multimedia and computer vision where such setting arise and 3) offer new directions and discuss about open questions that appear. In particular, the following topics are relevant:

  • Diversity / Complementarity / (Dis-)agreement between views
  • Incomplete / Noisy data
  • Missing labels / Noisy annotations
  • Multiview for Large-scale / Big data
  • Multiview and Ranking / Learn with imbalanced data set
  • Representation Learning with multiple views
  • Multiview for domain adaptation / transfer learning


The workshop will be based on invited talks, contributed talks and posters. In that respect, we have two submission types in LNCS format:

  • unpublished works (max 8 pages excluding references).
  • recently published works (extended abstract, max 4 pages). The extended abstract has to mention where and when the papers has been published.

Papers will be evaluated according to their originality and relevance to the workshop, and should include author names, affiliations, contact information, and an abstract. Accepted papers have to be presented orrally or as a poster, and will be available on the website.


  • Isabelle Guyon (ChaLearn)
  • Ludmila I. Kuncheva (Bangor University, UK)
  • John Shawe-Taylor (UCL, UK)


Workshop paper submission deadline: Friday, June 20, 2014

Workshop paper acceptance notification: Friday, July 11, 2014

Workshop paper camera-ready deadline: Friday, July 25, 2014

Workshop: Friday, September 19, 2014


  • Thierry Artières, UPMC Sorbonne University, Paris, France
  • Cécile Capponi, Aix-Marseille University, France
  • Frédéric Jurie, University of Caen, France
  • Hachem Kadri, Aix-Marseille University, France
  • Christoph Lampert, IST Austria, Austria
  • Stéphane Marchand-Maillet, University of Geneva, Switzerland
  • Bernard Merialdo, EURECOM, France
  • Georges Quénot, IMAG, France
  • Alain Rakotomamonjy, University of Rouen, France
  • Shin’ichi Satoh, NII, Japan
  • Cordelia Schmid, INRIA Rhône-Alpes Grenoble, France
  • Viktoriia Sharmanska, IST Austria, Austria


  • Stéphane Ayache, Aix-Marseille University, France.
  • Matthieu Cord, UPMC Sorbonne University, Paris, France.
  • François-Xavier Dupé, Aix-Marseille University, Marseille, France.
  • Emilie Morvant, Institute of Science and Technology (IST) Austria, Austria