Important Dates

  • Deadline for extended abstract submissions: tbd
  • Acceptance notification: tbd
  • Workshop: 13th of September 2018

Keynote Speakers

  • Kristen Grauman (UT Austin, United States)
    Kristen Grauman is a Professor in the Department of Computer Science at the University of Texas at Austin. Her research in computer vision and machine learning focuses on visual recognition and search. Before joining UT-Austin in 2007, she received her Ph.D. at MIT. She is an Alfred P. Sloan Research Fellow and Microsoft Research New Faculty Fellow, a recipient of NSF CAREER and ONR Young Investigator awards, the Regents' Outstanding Teaching Award from the University of Texas System in 2012, the PAMI Young Researcher Award in 2013, the 2013 Computers and Thought Award from the International Joint Conference on Artificial Intelligence (IJCAI), the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2013, and the Helmholtz Prize in 2017. She was inducted into the UT Academy of Distinguished Teachers in 2017. She and her collaborators were recognized with the CVPR Best Student Paper Award in 2008 for their work on hashing algorithms for large-scale image retrieval, the Marr Prize at ICCV in 2011 for their work on modeling relative visual attributes, the ACCV Best Application Paper Award in 2016 for their work on automatic cinematography for 360 degree video, and a Best Paper Honorable Mention at CHI in 2017 for work on crowds and visual question answering. She currently serves as an Associate Editor in Chief for the Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and as an Editorial Board member for the International Journal of Computer Vision (IJCV). She also served/serves as a Program Chair of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 and a Program Chair of Neural Information Processing Systems (NIPS) 2018.
  • Christoph Lampert (IST Austria)
    Christoph Lampert received the PhD degree in mathematics from the University of Bonn in 2003. In 2010 he joined the Institute of Science and Technology Austria (IST Austria) first as an Assistant Professor and since 2015 as a Professor. His research on computer vision and machine learning has won several international and national awards, including the best paper prize at CVPR 2008. In 2012 he was awarded an ERC Starting Grant by the European Research Council. He currently is an Editor of the International Journal of Computer Vision (IJCV), Action Editor of the Journal for Machine Learning Research (JMLR), and Associate Editor in Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
  • Joachim Denzler (University of Jena, Germany)
    Joachim Denzler earned the degrees “DiplomInformatiker”, “Dr.-Ing.” and “Habilitation” from the University of Erlangen, Germany, in years 1992, 1997, and 2003, respectively. Currently, he holds a position as full professor for computer science and is head of the Computer Vision Group at the Friedrich Schiller University Jena, Germany. He is also Director of the Michael Stifel Center for Data-Driven and Simulation Science, Jena. His research interests comprise the automatic analysis, fusion, and understanding of sensor data, especially development of methods for visual recognition tasks and dynamic scene analysis. He contributed in the area of active vision, 3D reconstruction, as well as object recognition and tracking. He is author and co-author of over 300 journal and conference papers as well as technical articles. He is a member of IEEE, IEEE computer society, DAGM, and GI.

Call for Abstracts and Participation

Learning algorithms are the backbone of computer vision research and still focused on training from large amounts of already annotated data. The limitations we are currently observing in many applications are mostly due to the lack of annotations or changing data distributions over time. To overcome these barriers, the annotation and learning of models needs to be coupled strongly through human-machine interaction. Furthermore, models need to adapt as needed to handle either shifts or completely novel data. The goal of this workshop is to discuss and present the advances in technologies that support annotation, model learning through expert guidance and continuous model adaptation.

  • Online and incremental learning
  • Interactive segmentation and detection to support annotation
  • Transfer learning
  • Active or self-taught
  • Continuous / lifelong learning
  • Open set learning
  • Open domain learning
  • Efficient fine-tuning of generic models
  • Personalization and customization
  • Privacy-preserving learning

Details about abstract submission will follow soon.