Over the years, computer scientists have primarily studied the information discovery process as a single user activity. For example, the research field of information retrieval has provided us with sophisticated techniques for supporting the search process, but mostly in the context of a single, isolated user’s interaction with an information base. The recent surge of interest in Knowledge Management, however, has contributed to enlarging the view on processes involving information discovery, sharing and reuse. Recent research now focuses as well on their cooperative aspects and methods for supporting these. In particular, a number of case studies have studied the cooperative nature of information search activities.
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Supporting collaborative information activities in networked communities
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| Nathalie Glance, Antonietta Grasso, Uwe Borghoff, Dave Snowdon, Jutta Willamowski |
| Bullinger, H.-J., Ziegler, J. (eds.): Proc. 8th Int’l. Conf. on Human-Computer Interaction (HCI’99), Vol. 2, Munich, Germany, August 22-27, 1999. Mahwah, NJ: Laurence Erlbaum Associates, Inc., pp. 422-426 |
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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