2 December 2013, Sydney, Australia

**Probabilistic graphical models** (PGMs) have been applied widely in computer
vision, to problems solving including image and video segmentation, scene
understanding, human activity recognition, tracking and point matching.
Inference methods for PGMs are a key enabler in many of these problems. With
advances in inference techniques, new insights are emerging, as are new
problems that are motivated by these applications. The purpose of this workshop
is to bring together an examination of theoretical advances in inference
techniques with emerging problem formulations motivated by applications.
Researchers are encouraged to submit work including novel inference methods,
new views or understandings, novel inference problems and/or
methods for solving them.

Solution of **Maximum a Posteriori** (MAP) problems has progressed rapidly in
recent years. Many inference methods are now generalized to higher-order
potentials via factorization over cliques or clusters, which bring convenience
for computer vision tasks where higher-order potentials are often desirable.
Dual decomposition and **Generalized Max Product for Linear Programming** (GMPLP)
are known to optimize the dual objective of the MAP **Linear Programming** (LP)
relaxation via subgradient or block coordinate descent. One attractive
property of these dual methods is that the optimality is guaranteed under
certain conditions such as zero gap between dual objective and the primal
objective value for the decoded solution and uniqueness of solutions over
intersections of clusters.

Smoothness
terms such as entropy can be added to prevent dual block coordinate descent
methods from becoming stuck in sub-optimal corners; convergence rates for
these methods have recently been reported. Apart from LP relaxations, many
other optimisation techniques yield useful relaxations including **Quadratic
Programming** (QP), **Semidefinite Programming** (SDP) and **Second-order Cone
Programming** (SOCP); each of these has been applied to inference problems.

Methods seeking approximations of the local marginal distributions include mean
field, **Expectation Propagation** (EP), loopy belief propagation (BP), generalized
BP, tree reweighted BP and norm product BP. The various BP algorithms have been
shown to be based on minimisation of the Bethe free energy, the Kikuchi
approximation, or convexifications thereof. Particularly successful
approximations have been found in a small number of canonical problems (e.g.,
approximation of the matrix permanent). Recently, progress has also
been made in marginal-MAP problems, which seek the MAP of a PGM following
marginalization of a subset of nodes. There has also been recent interest in
obtaining multiple high probability solutions from a model (Best-M), and our
workshop will include discussion on these approaches.

Despite the advances in this field, some important aspects are still unclear such as:

Convergence rates for non-smooth methods

Connections between various inference approaches, and which methods are suitable to which problems

Relationship between local marginal consistency and the graph structures

Constraint reduction

Decomposition of inference of a large PGM into a number of inferences of small PGMs

In approximate marginal inference, no guarantees exist on the error in the various solutions in comparison to the exact marginal distributions

Applications of inference also motivate new problem classes. Some examples are:

MAP inference with unknown graph structure. In the human activity recognition or scene understanding, the number of persons or objects across images or videos varies. To find the most probable labels for these persons or objects jointly, requires performing MAP inference without knowing the graph structure. This problem is different from a standard MAP inference problem where the graph structure is given. This is also different from estimating homogeneous graph structures where the graph structure is the same for all instances, or at least the basic structure across two consecutive time frames (two time-slice) is fixed.

Inference for large scale PGMs. For example, using PGMs to predict “social circles” in social networks such as Facebook encounters large scale PGMs. In video segmentation, the number of nodes is extremely high: for an one-hour video footage with a modest frame rate 15-frames-per-second and modest frame size 300 by 200 (treating each pixel as a node), there are over 3.2 billion nodes in the PGM.

Inference in hybrid continuous-discrete models. Tracking problems commonly involve both continuous variables (e.g., object positions) and discrete variables (e.g., object correspondences), and hard constraints in potentials. These complications have in part motivated a range of advances such as EP, non-parametric BP, marginal-MAP inference and merged mean field-BP approaches.

M-Best MAP problems and Diverse M-Best problems. These problems come from applications where, instead of a MAP solution, the top M most probable solutions or even diverse high-probability solutions are of interest.

All submissions should use the ICCV main conference paper format. Please check the ICCV main conference webpage for the paper templates.

All submissions (in PDF or MS Word format) should be emailed to Infer.PGM.iccv13@gmail.com with the email title being “ICCV13 PGM workshop submission”. Please include the authors’ names and affiliations in the email body. All submitted papers will receive at least two reviews.

All accepted submissions will be presented as oral plus poster or spotlight plus poster.

Each spotlight contains a 4-minute talk plus 1-minute question. No page limit for the slides as long as you can finish in time.

Each oral presentation contains a 19-minute talk plus 1-minute question.

Posters should follow the main conference's poster guideline.

Submission Deadline: | |

Notification of Acceptance: | 1 Oct. 2013 |

Final Versions Due: | 7 Oct. 2013 |

Workshop Date: | 2 Dec. 2013 |

Qinfeng (Javen) Shi | University of Adelaide, |

Chunhua Shen | University of Adelaide, |

Stephen Gould | Australian National University, |

Jason L. Williams | DSTO, |

Tiberio Caetano | NICTA, |

Dhruv Batra

Sebastian Nowozin

** Inference for probabilistic graphical models (PGMs) **

Date: 2 December 2013

Location: Room 103

14:00 Welcome and overview by the organisers

14:40 Invited talk 1

Title: Should we care about (MAP) Inference in Graphical Models? MAP Inference tools for more than MAP Inference by Dhruv Batra

15:20 Oral 1

Title: Supervised Hierarchical Dirichlet Process with Variational Inference

Authors: Cheng Zhang, Carl Henrik Ek, Xavi Gratal, Florian Pokorny, and Hedvig Kjellström

15:40 Coffee break

16:20 Invited talk 2

Title: Likelihood, Inference, and other hassles: Who needs graphical models? by Sebastian Nowozin

17:00 Oral 2

Title: Infinite Latent Conditional Random Fields

Authors: Yun Jiang and Ashutosh Saxena

17:20 Spotlight 1

Title: Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study

Authors: Bogdan Savchynskyy and Stefan Schmidt

17:25 Spotlight 2

Title: Superpixel Coherency and Uncertainty Models for Semantic Segmentation

Authors: SeungRyul Baek, Taegyu Lim, Yongseok Heo, Sungbum Park, Hantak Kwak, and Woosung Shim

17: 30 Spotlight 3

Title: Video Object Segmentation by Salient Segment Chain Composition

Authors: Dan Banica, Alexandru Agape, Adrian Ion, and Cristian Sminchisescu

17: 35 Poster session

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Otherwise, you may contact the Australian Embassy or Consulate in your country. See this webpage for more information.A letter of invitation can be issued once registration is done.