347 research outputs found
Video Propagation Networks
We propose a technique that propagates information forward through video
data. The method is conceptually simple and can be applied to tasks that
require the propagation of structured information, such as semantic labels,
based on video content. We propose a 'Video Propagation Network' that processes
video frames in an adaptive manner. The model is applied online: it propagates
information forward without the need to access future frames. In particular we
combine two components, a temporal bilateral network for dense and video
adaptive filtering, followed by a spatial network to refine features and
increased flexibility. We present experiments on video object segmentation and
semantic video segmentation and show increased performance comparing to the
best previous task-specific methods, while having favorable runtime.
Additionally we demonstrate our approach on an example regression task of color
propagation in a grayscale video.Comment: Appearing in Computer Vision and Pattern Recognition, 2017 (CVPR'17
Semantic Video CNNs through Representation Warping
In this work, we propose a technique to convert CNN models for semantic
segmentation of static images into CNNs for video data. We describe a warping
method that can be used to augment existing architectures with very little
extra computational cost. This module is called NetWarp and we demonstrate its
use for a range of network architectures. The main design principle is to use
optical flow of adjacent frames for warping internal network representations
across time. A key insight of this work is that fast optical flow methods can
be combined with many different CNN architectures for improved performance and
end-to-end training. Experiments validate that the proposed approach incurs
only little extra computational cost, while improving performance, when video
streams are available. We achieve new state-of-the-art results on the CamVid
and Cityscapes benchmark datasets and show consistent improvements over
different baseline networks. Our code and models will be available at
http://segmentation.is.tue.mpg.deComment: ICCV 201
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
Bilateral filters have wide spread use due to their edge-preserving
properties. The common use case is to manually choose a parametric filter type,
usually a Gaussian filter. In this paper, we will generalize the
parametrization and in particular derive a gradient descent algorithm so the
filter parameters can be learned from data. This derivation allows to learn
high dimensional linear filters that operate in sparsely populated feature
spaces. We build on the permutohedral lattice construction for efficient
filtering. The ability to learn more general forms of high-dimensional filters
can be used in several diverse applications. First, we demonstrate the use in
applications where single filter applications are desired for runtime reasons.
Further, we show how this algorithm can be used to learn the pairwise
potentials in densely connected conditional random fields and apply these to
different image segmentation tasks. Finally, we introduce layers of bilateral
filters in CNNs and propose bilateral neural networks for the use of
high-dimensional sparse data. This view provides new ways to encode model
structure into network architectures. A diverse set of experiments empirically
validates the usage of general forms of filters
Consensus Message Passing for Layered Graphical Models
Generative models provide a powerful framework for probabilistic reasoning.
However, in many domains their use has been hampered by the practical
difficulties of inference. This is particularly the case in computer vision,
where models of the imaging process tend to be large, loopy and layered. For
this reason bottom-up conditional models have traditionally dominated in such
domains. We find that widely-used, general-purpose message passing inference
algorithms such as Expectation Propagation (EP) and Variational Message Passing
(VMP) fail on the simplest of vision models. With these models in mind, we
introduce a modification to message passing that learns to exploit their
layered structure by passing 'consensus' messages that guide inference towards
good solutions. Experiments on a variety of problems show that the proposed
technique leads to significantly more accurate inference results, not only when
compared to standard EP and VMP, but also when compared to competitive
bottom-up conditional models.Comment: Appearing in Proceedings of the 18th International Conference on
Artificial Intelligence and Statistics (AISTATS) 201
A case of formic acid poisoning: prompting for a different line of management
Formic acid is an easily available substance and can thus be consumed voluntarily or accidentally. Here is a case who presented to our center 5 days post accidental ingestion of formic acid. He had developed acute renal failure requiring hemodialysis. Post admission he had altered sensorium probably alcohol withdrawal and later aspiration pneumonia and ARDS. He developed a late onset GI bleed on day 8 of ingestion with significant hemoglobin drop. An emergency endoscopy was done which showed a normal esophagus but extensive corrosive damage and active bleeding from gastric area. He continued to have drop in hemoglobin in-spite of aggressive volume and blood product transfusions. He was taken up for an emergency subtotal gastrectomy and viable tissue was demarcated with intraoperative endoscopy and a subtotal gastrectomy was performed. He stabilized initially but worsened again 2 days later with worsening lactic acidosis and succumbed to his illness. The fact that esophagus was completely spared with extensive involvement of stomach could possibly indicate a need for review of initial management of corrosive/organic acid poisoning
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