981 research outputs found
Bethe Projections for Non-Local Inference
Many inference problems in structured prediction are naturally solved by
augmenting a tractable dependency structure with complex, non-local auxiliary
objectives. This includes the mean field family of variational inference
algorithms, soft- or hard-constrained inference using Lagrangian relaxation or
linear programming, collective graphical models, and forms of semi-supervised
learning such as posterior regularization. We present a method to
discriminatively learn broad families of inference objectives, capturing
powerful non-local statistics of the latent variables, while maintaining
tractable and provably fast inference using non-Euclidean projected gradient
descent with a distance-generating function given by the Bethe entropy. We
demonstrate the performance and flexibility of our method by (1) extracting
structured citations from research papers by learning soft global constraints,
(2) achieving state-of-the-art results on a widely-used handwriting recognition
task using a novel learned non-convex inference procedure, and (3) providing a
fast and highly scalable algorithm for the challenging problem of inference in
a collective graphical model applied to bird migration.Comment: minor bug fix to appendix. appeared in UAI 201
GPstruct: Bayesian structured prediction using Gaussian processes
We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M ^3 N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct
Learning Deep Structured Models
Many problems in real-world applications involve predicting several random
variables which are statistically related. Markov random fields (MRFs) are a
great mathematical tool to encode such relationships. The goal of this paper is
to combine MRFs with deep learning algorithms to estimate complex
representations while taking into account the dependencies between the output
random variables. Towards this goal, we propose a training algorithm that is
able to learn structured models jointly with deep features that form the MRF
potentials. Our approach is efficient as it blends learning and inference and
makes use of GPU acceleration. We demonstrate the effectiveness of our
algorithm in the tasks of predicting words from noisy images, as well as
multi-class classification of Flickr photographs. We show that joint learning
of the deep features and the MRF parameters results in significant performance
gains.Comment: 11 pages including referenc
Structured learning of sum-of-submodular higher order energy functions
Submodular functions can be exactly minimized in polynomial time, and the
special case that graph cuts solve with max flow \cite{KZ:PAMI04} has had
significant impact in computer vision
\cite{BVZ:PAMI01,Kwatra:SIGGRAPH03,Rother:GrabCut04}. In this paper we address
the important class of sum-of-submodular (SoS) functions
\cite{Arora:ECCV12,Kolmogorov:DAM12}, which can be efficiently minimized via a
variant of max flow called submodular flow \cite{Edmonds:ADM77}. SoS functions
can naturally express higher order priors involving, e.g., local image patches;
however, it is difficult to fully exploit their expressive power because they
have so many parameters. Rather than trying to formulate existing higher order
priors as an SoS function, we take a discriminative learning approach,
effectively searching the space of SoS functions for a higher order prior that
performs well on our training set. We adopt a structural SVM approach
\cite{Joachims/etal/09a,Tsochantaridis/etal/04} and formulate the training
problem in terms of quadratic programming; as a result we can efficiently
search the space of SoS priors via an extended cutting-plane algorithm. We also
show how the state-of-the-art max flow method for vision problems
\cite{Goldberg:ESA11} can be modified to efficiently solve the submodular flow
problem. Experimental comparisons are made against the OpenCV implementation of
the GrabCut interactive segmentation technique \cite{Rother:GrabCut04}, which
uses hand-tuned parameters instead of machine learning. On a standard dataset
\cite{Gulshan:CVPR10} our method learns higher order priors with hundreds of
parameter values, and produces significantly better segmentations. While our
focus is on binary labeling problems, we show that our techniques can be
naturally generalized to handle more than two labels
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
This paper proposes a new hybrid architecture that consists of a deep
Convolutional Network and a Markov Random Field. We show how this architecture
is successfully applied to the challenging problem of articulated human pose
estimation in monocular images. The architecture can exploit structural domain
constraints such as geometric relationships between body joint locations. We
show that joint training of these two model paradigms improves performance and
allows us to significantly outperform existing state-of-the-art techniques
Block Belief Propagation for Parameter Learning in Markov Random Fields
Traditional learning methods for training Markov random fields require doing
inference over all variables to compute the likelihood gradient. The iteration
complexity for those methods therefore scales with the size of the graphical
models. In this paper, we propose \emph{block belief propagation learning}
(BBPL), which uses block-coordinate updates of approximate marginals to compute
approximate gradients, removing the need to compute inference on the entire
graphical model. Thus, the iteration complexity of BBPL does not scale with the
size of the graphs. We prove that the method converges to the same solution as
that obtained by using full inference per iteration, despite these
approximations, and we empirically demonstrate its scalability improvements
over standard training methods.Comment: Accepted to AAAI 201
Learning Structured Inference Neural Networks with Label Relations
Images of scenes have various objects as well as abundant attributes, and
diverse levels of visual categorization are possible. A natural image could be
assigned with fine-grained labels that describe major components,
coarse-grained labels that depict high level abstraction or a set of labels
that reveal attributes. Such categorization at different concept layers can be
modeled with label graphs encoding label information. In this paper, we exploit
this rich information with a state-of-art deep learning framework, and propose
a generic structured model that leverages diverse label relations to improve
image classification performance. Our approach employs a novel stacked label
prediction neural network, capturing both inter-level and intra-level label
semantics. We evaluate our method on benchmark image datasets, and empirical
results illustrate the efficacy of our model.Comment: Conference on Computer Vision and Pattern Recognition(CVPR) 201
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