6,584 research outputs found
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Dense conditional random fields (CRFs) have become a popular framework for
modelling several problems in computer vision such as stereo correspondence and
multi-class semantic segmentation. By modelling long-range interactions, dense
CRFs provide a labelling that captures finer detail than their sparse
counterparts. Currently, the state-of-the-art algorithm performs mean-field
inference using a filter-based method but fails to provide a strong theoretical
guarantee on the quality of the solution. A question naturally arises as to
whether it is possible to obtain a maximum a posteriori (MAP) estimate of a
dense CRF using a principled method. Within this paper, we show that this is
indeed possible. We will show that, by using a filter-based method, continuous
relaxations of the MAP problem can be optimised efficiently using
state-of-the-art algorithms. Specifically, we will solve a quadratic
programming (QP) relaxation using the Frank-Wolfe algorithm and a linear
programming (LP) relaxation by developing a proximal minimisation framework. By
exploiting labelling consistency in the higher-order potentials and utilising
the filter-based method, we are able to formulate the above algorithms such
that each iteration has a complexity linear in the number of classes and random
variables. The presented algorithms can be applied to any labelling problem
using a dense CRF with sparse higher-order potentials. In this paper, we use
semantic segmentation as an example application as it demonstrates the ability
of the algorithm to scale to dense CRFs with large dimensions. We perform
experiments on the Pascal dataset to indicate that the presented algorithms are
able to attain lower energies than the mean-field inference method
Multi-Modal Mean-Fields via Cardinality-Based Clamping
Mean Field inference is central to statistical physics. It has attracted much
interest in the Computer Vision community to efficiently solve problems
expressible in terms of large Conditional Random Fields. However, since it
models the posterior probability distribution as a product of marginal
probabilities, it may fail to properly account for important dependencies
between variables. We therefore replace the fully factorized distribution of
Mean Field by a weighted mixture of such distributions, that similarly
minimizes the KL-Divergence to the true posterior. By introducing two new
ideas, namely, conditioning on groups of variables instead of single ones and
using a parameter of the conditional random field potentials, that we identify
to the temperature in the sense of statistical physics to select such groups,
we can perform this minimization efficiently. Our extension of the clamping
method proposed in previous works allows us to both produce a more descriptive
approximation of the true posterior and, inspired by the diverse MAP paradigms,
fit a mixture of Mean Field approximations. We demonstrate that this positively
impacts real-world algorithms that initially relied on mean fields.Comment: Submitted for review to CVPR 201
Fitting heavy tailed distributions: the poweRlaw package
Over the last few years, the power law distribution has been used as the data
generating mechanism in many disparate fields. However, at times the techniques
used to fit the power law distribution have been inappropriate. This paper
describes the poweRlaw R package, which makes fitting power laws and other
heavy-tailed distributions straightforward. This package contains R functions
for fitting, comparing and visualising heavy tailed distributions. Overall, it
provides a principled approach to power law fitting.Comment: The code for this paper can be found at
https://github.com/csgillespie/poweRla
DOPE: Distributed Optimization for Pairwise Energies
We formulate an Alternating Direction Method of Mul-tipliers (ADMM) that
systematically distributes the computations of any technique for optimizing
pairwise functions, including non-submodular potentials. Such discrete
functions are very useful in segmentation and a breadth of other vision
problems. Our method decomposes the problem into a large set of small
sub-problems, each involving a sub-region of the image domain, which can be
solved in parallel. We achieve consistency between the sub-problems through a
novel constraint that can be used for a large class of pair-wise functions. We
give an iterative numerical solution that alternates between solving the
sub-problems and updating consistency variables, until convergence. We report
comprehensive experiments, which demonstrate the benefit of our general
distributed solution in the case of the popular serial algorithm of Boykov and
Kolmogorov (BK algorithm) and, also, in the context of non-submodular
functions.Comment: Accepted at CVPR 201
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the primal and the dual problems is however a more recent idea which has
generated many important new contributions in the last years. These novel
developments are grounded on recent advances in convex analysis, discrete
optimization, parallel processing, and non-smooth optimization with emphasis on
sparsity issues. In this paper, we aim at presenting the principles of
primal-dual approaches, while giving an overview of numerical methods which
have been proposed in different contexts. We show the benefits which can be
drawn from primal-dual algorithms both for solving large-scale convex
optimization problems and discrete ones, and we provide various application
examples to illustrate their usefulness
A Conditional Random Field for Multiple-Instance Learning
We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines discriminative unary instance classifiers and pairwise dissimilarity measures. We show that both forces improve the classification performance. Unlike other approaches, MI-CRF considers all bags jointly during training as well as during testing. This makes it possible to classify test bags in an imputation setup. The parameters of MI-CRF are learned using constraint generation. Furthermore, we show that MI-CRF can incorporate previous MIL algorithms to improve on their results. MI-CRF obtains competitive results on five standard MIL datasets. 1
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