8,604 research outputs found
End-to-end representation learning for Correlation Filter based tracking
The Correlation Filter is an algorithm that trains a linear template to
discriminate between images and their translations. It is well suited to object
tracking because its formulation in the Fourier domain provides a fast
solution, enabling the detector to be re-trained once per frame. Previous works
that use the Correlation Filter, however, have adopted features that were
either manually designed or trained for a different task. This work is the
first to overcome this limitation by interpreting the Correlation Filter
learner, which has a closed-form solution, as a differentiable layer in a deep
neural network. This enables learning deep features that are tightly coupled to
the Correlation Filter. Experiments illustrate that our method has the
important practical benefit of allowing lightweight architectures to achieve
state-of-the-art performance at high framerates.Comment: To appear at CVPR 201
Learning shape correspondence with anisotropic convolutional neural networks
Establishing correspondence between shapes is a fundamental problem in
geometry processing, arising in a wide variety of applications. The problem is
especially difficult in the setting of non-isometric deformations, as well as
in the presence of topological noise and missing parts, mainly due to the
limited capability to model such deformations axiomatically. Several recent
works showed that invariance to complex shape transformations can be learned
from examples. In this paper, we introduce an intrinsic convolutional neural
network architecture based on anisotropic diffusion kernels, which we term
Anisotropic Convolutional Neural Network (ACNN). In our construction, we
generalize convolutions to non-Euclidean domains by constructing a set of
oriented anisotropic diffusion kernels, creating in this way a local intrinsic
polar representation of the data (`patch'), which is then correlated with a
filter. Several cascades of such filters, linear, and non-linear operators are
stacked to form a deep neural network whose parameters are learned by
minimizing a task-specific cost. We use ACNNs to effectively learn intrinsic
dense correspondences between deformable shapes in very challenging settings,
achieving state-of-the-art results on some of the most difficult recent
correspondence benchmarks
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Efficient simulation of the Navier-Stokes equations for fluid flow is a long
standing problem in applied mathematics, for which state-of-the-art methods
require large compute resources. In this work, we propose a data-driven
approach that leverages the approximation power of deep-learning with the
precision of standard solvers to obtain fast and highly realistic simulations.
Our method solves the incompressible Euler equations using the standard
operator splitting method, in which a large sparse linear system with many free
parameters must be solved. We use a Convolutional Network with a highly
tailored architecture, trained using a novel unsupervised learning framework to
solve the linear system. We present real-time 2D and 3D simulations that
outperform recently proposed data-driven methods; the obtained results are
realistic and show good generalization properties.Comment: Significant revisio
- …