Perception problems are hard. Computer Vision systems must deal with significant levels of ambiguity -
from inter- and intra-object occlusion and varying appearance, lighting, and pose. Probabilistic models
provide a principled framework for dealing with uncertainty and for converting evidence into a posteriori
belief about the world. Typically, a vision system uses this belief to predict the "most likely"
or maximum a-posteriori hypothesis. Unfortunately, our current models are inaccurate and this single-best
hypothesis is often incorrect.
The overarching goal of this work is to allow vision systems to hedge against uncertainty by producing
multiple plausible hypotheses. Specifically, this project develops techniques for finding a diverse
set of high-probability solutions from probabilistic models. The project focuses on (a) interactive
object cutout (where multiple segmentations are shown to the user to expedite convergence to an acceptable result);
(b) semantic segmentation (where multiple plausible scene labelings are propagated to subsequent
stages of a cascade for higher-order processing); (c) person/object tracking (where multiple
localization hypotheses on each frame reduce the search space of a sequence tracker).
This material is based upon work supported by the National Science Foundation under Grant No. 1353694.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or Virginia Tech.