86 research outputs found
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
A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks
We present a fast algorithm for training MaxPooling Convolutional Networks to
segment images. This type of network yields record-breaking performance in a
variety of tasks, but is normally trained on a computationally expensive
patch-by-patch basis. Our new method processes each training image in a single
pass, which is vastly more efficient.
We validate the approach in different scenarios and report a 1500-fold
speed-up. In an application to automated steel defect detection and
segmentation, we obtain excellent performance with short training times
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
This paper introduces Non-Autonomous Input-Output Stable Network (NAIS-Net),
a very deep architecture where each stacked processing block is derived from a
time-invariant non-autonomous dynamical system. Non-autonomy is implemented by
skip connections from the block input to each of the unrolled processing stages
and allows stability to be enforced so that blocks can be unrolled adaptively
to a pattern-dependent processing depth. NAIS-Net induces non-trivial,
Lipschitz input-output maps, even for an infinite unroll length. We prove that
the network is globally asymptotically stable so that for every initial
condition there is exactly one input-dependent equilibrium assuming tanh units,
and multiple stable equilibria for ReL units. An efficient implementation that
enforces the stability under derived conditions for both fully-connected and
convolutional layers is also presented. Experimental results show how NAIS-Net
exhibits stability in practice, yielding a significant reduction in
generalization gap compared to ResNets.Comment: NIPS 201
ReConvNet: Video Object Segmentation with Spatio-Temporal Features Modulation
We introduce ReConvNet, a recurrent convolutional architecture for
semi-supervised video object segmentation that is able to fast adapt its
features to focus on any specific object of interest at inference time.
Generalization to new objects never observed during training is known to be a
hard task for supervised approaches that would need to be retrained. To tackle
this problem, we propose a more efficient solution that learns spatio-temporal
features self-adapting to the object of interest via conditional affine
transformations. This approach is simple, can be trained end-to-end and does
not necessarily require extra training steps at inference time. Our method
shows competitive results on DAVIS2016 with respect to state-of-the art
approaches that use online fine-tuning, and outperforms them on DAVIS2017.
ReConvNet shows also promising results on the DAVIS-Challenge 2018 winning the
-th position.Comment: CVPR Workshop - DAVIS Challenge 201
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