HiPot 2012 - Workshop on Higher-Order Models and Global Constraints in Computer Vision

Many tasks in computer vision, including low-level ones such as image segmentation, stereo estimation, as well as high-level ones such as object recognition, scene understanding, have been modelled as discrete labelling problems. Furthermore, discrete optimization has emerged as an indispensable tool to solve these problems over the last two decades. It is now a routine process to write an explicit energy function, understand the Bayesian priors it incorporates, and then depending on its properties, perform exact or approximate inference.
Initially, one of the popular ways to model a labelling problem has been in terms of an energy function comprising of unary and pairwise clique potentials. This assumption severely restricts the representational power of these models as they are unable to capture the rich statistics of natural images. More recently, a second wave of success can be attributed to the incorporation of higher-order terms that have the ability to encode significantly more sophisticated priors and structural dependencies between variables ? e.g., second-order smoothness priors in stereo, priors on natural image statistics for de-noising, robust smoothness priors for object labelling, co-occurrence priors for object category segmentation, connectivity and bounding-box priors for image segmentation.
The goal of this workshop is to bring together researchers working on different aspects of this problem (modelling, inference and learning) and discuss various techniques, common solutions, open questions and future pursuits, such as:
Modelling
(a) What other forms of higher order potentials can be used (e.g. grammar-based)?
(b) Which image priors should we aim to model?
Inference
(c) How feasible is it to extend the class of functions exactly solvable?
(d) Given the "satisfactory" results of many approximate algorithms, what more can we gain from exact solutions?
(e) Can we find theoretical upper bound for the approximate solutions?
(f) How do we compare the various inference methods?
Learning
(g) How do we learn with higher-order potentials and global constraints?
(h) Should we explore piece-wise or distributed or coarse-to-fine learning?
Important Dates
Paper submission Deadline July 5
Acceptance Notification July 25
Camera Ready Due August 1
Workshop Date October 13
https://sites.google.com/a/ttic.edu/eccv-2012-work...