Julian McAuley (NICTA/ANU) ua.moc.atcin@yeluacm.nailuj

Tibério Caetano (NICTA/ANU) ua.moc.atcin@onateac.oirebit

Pushmeet Kohli (MSRC) moc.tfosorcim@ilhokp

Pawan Kumar (Stanford)

Stephen Gould (ANU) ude.drofnats.ia@dluogs


Accepted Papers



Graphical models are now ubiquitous in numerous computer vision applications from low-level problems such as multi-class image labeling and stereo reconstruction to high-level problems such as articulated object detection. Many of these problems exhibit specific structure, such as regular neighborhoods or submodular potentials, that can be exploited to reduce worst-case inference complexity. Many of these complexity results are published in distinct communities (possibly even outside of computer vision); this workshop aims to combine these results under a common theme.

Schedule - June 20

8:50: Introduction

9:05: Pedro Felzenszwalb

Fast Inference with Min-Sum Matrix Product

The MAP inference problem in many graphical models can be solved efficiently using a fast algorithm for computing min-sum products of n by n matrices. The class of models in question includes cyclic and skip-chain models that arise in many applications. Although the worst-case complexity of the min-sum product operation is not known to be much better than O(n^3), an O(n^2.5) expected time algorithm was recently given, subject to some constraints on the input matrices. In this talk I will describe an algorithm that runs in O(n^2 log n) expected time, assuming the entries in the input matrices are independent samples from a uniform distribution. I will also show that two variants of the algorithm are quite fast for inputs that arise in several applications. This leads to significant performance gains over previous methods in applications within computer vision and natural language processing. This is joint work with Julian McAuley.

9:40: Richard Zemel

Learning and Inference to Exploit High-Order Potentials

An important challenge is to develop models that can capture a variety of structure in the real world, and can effectively represent our prior conceptions of this structure, as well as our objectives for the model. Many such structures are high-order, in that they involve interactions between many variables, and as such cannot be utilized efficiently in standard graphical models. In a number of practical settings, however, high order interactions can be utilized efficiently by leveraging specific within-factor structure. We explore two applications of this tractability: high-order models, and high-order losses. Both rely on a common underlying factor graph formulation, with a form of max-product for inference. This method is simple, modular, and flexible; it is easy to define new factors, and experimental results are good on a range of problems of various sizes. I will discuss a few projects within this framework, focusing on image labeling as an application example: learning to optimize counts potentials; using a suite of high-order potentials for joint object and depth labeling; and learning using high-order losses.

10:15: Coffee break

10:40: Dhruv Batra

Focussed Inference in Markov Random Fields with Local Primal-Dual Gaps

Markov Random Fields have become the workhorses of modern machine learning and computer vision. A number of situations require Dynamic Adaptive Inference algorithms that must adapt and reorder computations to "important" parts of the problem. In this talk I will describe one such measure for identifying important parts of the problem -- called Local Primal Dual Gaps (LPDG). LPDG is based on complementary slackness conditions in the Primal-Dual pair of Linear Programs (LP) in the LP relaxation of MAP inference. We have found LPDG to be useful in a number of situations -- speeding-up message-passing algorithms by re-ordering message computations (Tarlow et al. ICML '11), speeding up alpha-expansion by re-ordering label sweeps (Batra & Kohli CVPR '11) and adaptive tightening of the standard LP relaxation by choosing important constraints to add (Batra et al. AISTATS '11). Joint work with Pushmeet Kohli (MSRC), Sebastian Nowozin (MSRC), Daniel Tarlow (UToronto) and Vladimir Kolmogorov (UCL).

11:15: Spotlights

Two-minute spotlights by Chang-Dong Wang, Steve Gu, Stefanie Jegelka, Julian Yarkony, Dongjin Kwon, Kyong Joon Lee, and Danny Tarlow

11:30: Poster session, followed by lunch

1:30: Sebastian Nowozin

From Potentials to Polyhedra: Inference in Structured Models

Many computer vision tasks such as scene understanding and object recognition are nowadays routinely addressed with sophisticated statistical models for discrete variables. The most efficient algorithms for MAP inference in these models are based on linear programming relaxations to linear integer programming formulations. This establishes a connection between problems of discrete inference and the study of integral polytopes, that is, the field of polyhedral combinatorics. In this talk I distinguish decomposable and non-decomposable higher-order interactions in discrete models and describe how most structured potentials are interpretable as so called 'extended formulation' integer programs. For probabilistic inference the situation is different, and generally harder.

2:05: Nikos Komodakis

Fast Training of Pairwise or Higher-order MRFs

Markov Random Fields are ubiquitous in computer vision, which is why related MAP inference algorithms have attracted a significant amount of research over the past years. However, besides MAP estimation, the task of learning the parameters of an MRF plays an equally, if not more, important role for successfully applying the resulting models to computer vision problems. In this talk I will present a very efficient learning algorithm for this task. The proposed method is quite general as well as very flexible, and it can be easily applied to both pairwise and higher-order MRF models.

2:40: Nebojsa Jojic

Accepted Papers

Message-Passing for the Traveling Salesman Problem
Chang-Dong Wang, Jian-Huang Lai, Wei-Shi Zheng

Extended Pairwise Potentials
Steve Gu, Ying Zheng, Carlo Tomasi

Multi-label cooperative cuts
Stefanie Jegelka, Jeff Bilmes

Planar Decompositions and Cycle Constraints
Julian Yarkony, Ragib Morshed, Alexander T. Ihler, Charless C. Fowlkes

Nonrigid Image Registration Using a Higher-Order MRF Model with Dense Local Descriptors
Dongjin Kwon, Kyong Joon Lee, Il Dong Yun, Sang Uk Lee

Inference with l0-norm-based Sparsity Prior on Discrete Frameworks
Kyong Joon Lee, Dongjin Kwon, Il Dong Yun, Sang Uk Lee

Big and Tall: Large Margin Learning with High Order Losses
Daniel Tarlow, Richard Zemel


Graph cuts
Y. Boykov, V. Kolmogorov

Belief propagation for early vision
P.F. Felzenszwalb, D.P. Huttenlocher

Fast-PD MRF optimization
N. Komodakis, G. Tziritas, N. Paragios

Learning for high-level vision
S. Nowozin

Message-passing for LP relaxations
D. Sontag

Structured potentials for dual decomposition-based inference
D. Sontag, A. Globerson, T. Jaakkola

Inference in graphical models (Matlab interfaces)
S. Bagon

Workshop References

Beyond trees: MRF inference via outer-planar decomposition
D. Batra, A.C. Gallagher, D. Parikh, T.H. Chen, CVPR 2010

Fast approximate energy minimization via graph cuts
Y. Boykov, O. Veksler, R. Zabih PAMI 2004

Residual belief propagation: Informed scheduling for asynchronous message passing
G. Elidan, UAI 2006

Efficient belief propagation for early vision
P. F. Felzenszwalb, D. P. Huttenlocher, IJCV 2006

Fast inference with min-sum matrix product
P. Felzenszwalb, J. McAuley Technical Report, University of Chicago, 2010

Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
S. Geman, D. Geman, PAMI 1984

Exact optimization for Markov Random Fields with convex priors
H. Ishikawa, PAMI 2003

Counting belief propagation
K. Kersting, B. Ahmadi, S. Natarajan, UAI 2009

Inference in Bayesian networks using nested junction trees
U. Kjærulff, Proceedings of the NATO Advanced Study Institute on Learning in graphical models, 1998

What energy functions can be minimized via graph cuts?
V. Kolmogorov, R. Zabih, ECCV 2002

Exploiting data-independence for fast belief-propagation
J. J. McAuley, T. S. Caetano, ICML 2010

A differential semantics for jointree algorithms
J. D. Park, A. Darwiche, NIPS 2003

Fast generalized belief propagation for MAP estimation on 2D and 3D grid-like Markov Random Fields
K. Petersen, J. Fehr, H. Burkhardt, DAGM 2008

Syntactic analysis of two-dimensional visual signals in the presence of noise
M. I. Shlezinger, Kibernetika 1976

Graph cut based optimization for MRFs with truncated convex priors
O. Veksler, CVPR 2007

Questions and comments to moc.liamg@yeluacm.nailuj. Last edited 26/6/11