Abstract. This paper presents an architecture that enables the recognizer to learn incrementally and, thereby adapt to document image collections for performance improvement. We ar...
Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples...
In this paper, we propose a Bayesian learning approach to promoting diversity for information retrieval in biomedicine and a re-ranking model to improve retrieval performance in t...
— Learning parameters of a motion model is an important challenge for autonomous robots. We address the particular instance of parameter learning when tracking motions with a swi...
A variety of flexible models have been proposed to detect
objects in challenging real world scenes. Motivated
by some of the most successful techniques, we propose a
hierarchica...
Paul Schnitzspan (TU Darmstadt), Mario Fritz (Univ...