42,848 research outputs found
Stochastic Variational Inference with Gradient Linearization
Variational inference has experienced a recent surge in popularity owing to
stochastic approaches, which have yielded practical tools for a wide range of
model classes. A key benefit is that stochastic variational inference obviates
the tedious process of deriving analytical expressions for closed-form variable
updates. Instead, one simply needs to derive the gradient of the log-posterior,
which is often much easier. Yet for certain model classes, the log-posterior
itself is difficult to optimize using standard gradient techniques. One such
example are random field models, where optimization based on gradient
linearization has proven popular, since it speeds up convergence significantly
and can avoid poor local optima. In this paper we propose stochastic
variational inference with gradient linearization (SVIGL). It is similarly
convenient as standard stochastic variational inference - all that is required
is a local linearization of the energy gradient. Its benefit over stochastic
variational inference with conventional gradient methods is a clear improvement
in convergence speed, while yielding comparable or even better variational
approximations in terms of KL divergence. We demonstrate the benefits of SVIGL
in three applications: Optical flow estimation, Poisson-Gaussian denoising, and
3D surface reconstruction.Comment: To appear at CVPR 201
Disparity and Optical Flow Partitioning Using Extended Potts Priors
This paper addresses the problems of disparity and optical flow partitioning
based on the brightness invariance assumption. We investigate new variational
approaches to these problems with Potts priors and possibly box constraints.
For the optical flow partitioning, our model includes vector-valued data and an
adapted Potts regularizer. Using the notation of asymptotically level stable
functions we prove the existence of global minimizers of our functionals. We
propose a modified alternating direction method of minimizers. This iterative
algorithm requires the computation of global minimizers of classical univariate
Potts problems which can be done efficiently by dynamic programming. We prove
that the algorithm converges both for the constrained and unconstrained
problems. Numerical examples demonstrate the very good performance of our
partitioning method
Self-Evaluation Applied Mathematics 2003-2008 University of Twente
This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
In this work, we propose a novel and efficient method for articulated human
pose estimation in videos using a convolutional network architecture, which
incorporates both color and motion features. We propose a new human body pose
dataset, FLIC-motion, that extends the FLIC dataset with additional motion
features. We apply our architecture to this dataset and report significantly
better performance than current state-of-the-art pose detection systems
FlowNet: Learning Optical Flow with Convolutional Networks
Convolutional neural networks (CNNs) have recently been very successful in a
variety of computer vision tasks, especially on those linked to recognition.
Optical flow estimation has not been among the tasks where CNNs were
successful. In this paper we construct appropriate CNNs which are capable of
solving the optical flow estimation problem as a supervised learning task. We
propose and compare two architectures: a generic architecture and another one
including a layer that correlates feature vectors at different image locations.
Since existing ground truth data sets are not sufficiently large to train a
CNN, we generate a synthetic Flying Chairs dataset. We show that networks
trained on this unrealistic data still generalize very well to existing
datasets such as Sintel and KITTI, achieving competitive accuracy at frame
rates of 5 to 10 fps.Comment: Added supplementary materia
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