2,615 research outputs found
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
We tackle the task of semi-supervised video object segmentation, i.e.
segmenting the pixels belonging to an object in the video using the ground
truth pixel mask for the first frame. We build on the recently introduced
one-shot video object segmentation (OSVOS) approach which uses a pretrained
network and fine-tunes it on the first frame. While achieving impressive
performance, at test time OSVOS uses the fine-tuned network in unchanged form
and is not able to adapt to large changes in object appearance. To overcome
this limitation, we propose Online Adaptive Video Object Segmentation (OnAVOS)
which updates the network online using training examples selected based on the
confidence of the network and the spatial configuration. Additionally, we add a
pretraining step based on objectness, which is learned on PASCAL. Our
experiments show that both extensions are highly effective and improve the
state of the art on DAVIS to an intersection-over-union score of 85.7%.Comment: Accepted at BMVC 2017. This version contains minor changes for the
camera ready versio
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
A Survey of Multimedia Technologies and Robust Algorithms
Multimedia technologies are now more practical and deployable in real life,
and the algorithms are widely used in various researching areas such as deep
learning, signal processing, haptics, computer vision, robotics, and medical
multimedia processing. This survey provides an overview of multimedia
technologies and robust algorithms in multimedia data processing, medical
multimedia processing, human facial expression tracking and pose recognition,
and multimedia in education and training. This survey will also analyze and
propose a future research direction based on the overview of current robust
algorithms and multimedia technologies. We want to thank the research and
previous work done by the Multimedia Research Centre (MRC), the University of
Alberta, which is the inspiration and starting point for future research.Comment: arXiv admin note: text overlap with arXiv:2010.1296
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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