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 speciﬁc
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
* 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.html
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).
Julian McAuley, Tiberio Caetano, Pushmeet Kohli, Pawan Kumar, Stephen Gould