Sparse, High Dimensional Filtering
Peter Gehler
(U. of Tuebingen, Germany)
Abstract:
In this talk I will present our recent work on learning filters for sparse and
high dimensional signals. In particular we generalize the parametrization of the
widely used bilateral filter and derive an algorithm so filter parameters can be
learned from data. This generalization has interesting consequences for a wide
range of computer vision problems and learning architectures. In particular it
allows for CNN architectures that have no spatial but higher dimensional
organization of intermediate representations. These can be used to naturally
handle sparse input data. I will present results on several computer vision
tasks, including image segmentation, and image filtering applications. For the
task of semantic image segmentation I will demonstrate how this type of
filtering can replace the recent so-called dense CRF inference approaches. It
effectively removes the need for CNN-CRF combinations that are in common for
segmentation.