CVPR 2011 - CVPR 2011 Workshop on Inference in Graphical Models with Structured Potentials

CVPR 2011 Workshop on Inference in Graphical Models
with Structured Potentials

In this workshop, we aim to bring together researchers working on
inference problems in computer vision and pattern recognition, in
which the specific 'structures' that arise in real applications allow
for reduced complexity or increased accuracy.

Well-known examples include submodularity, sparsity, and convexity.
However, there are numerous lesser-known yet important results:
exploiting shared potentials; choosing message-passing schemes based
on speci?c inputs; exploiting potential functions that are
'truncated'; exploiting topology in bipartite, planar, or grid-like
models; exploiting potential functions that factorize.

Among these ideas there is a common theme: the structure of energy
functions that arise in computer vision applications often allows for
far better performance than the pessimistic results offered by
standard inference procedures.

We invite submissions in the following areas:

* Exact and approximate inference in graphical models
* Exploiting graph topology: bipartite graphs; planar graphs; grid
models (etc.)
* Submodularity, sparsity, convexity
* Message passing: messages that factorize; repeated messages; message-
passing schemes (etc.)
* Higher-order potentials for image labeling
* Other types of structure: shared potentials; low-order potentials
(etc.)

Submissions to other areas are also encouraged. Each accepted
submission will be included in a poster session and a 'spotlight-
style' presentation. We also invite authors of inference code and
other resources to participate in our spotlight session.

Participants are invited to submit 4-page extended abstracts in the
CVPR 2011 format by April 15. Further details may be found on the
workshop webpage: http://users.cecs.anu.edu.au/~julianm/cvpr2011.htm...

If you have any questions or comments, or wish to have a resource
added to our webpage, please e-mail Julian McAuley
(julian.mcauley-AT-gmail.com).