1,853 research outputs found
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Deep convolutional neural networks (CNNs) have shown excellent performance in
object recognition tasks and dense classification problems such as semantic
segmentation. However, training deep neural networks on large and sparse
datasets is still challenging and can require large amounts of computation and
memory. In this work, we address the task of performing semantic segmentation
on large data sets, such as three-dimensional medical images. We propose an
adaptive sampling scheme that uses a-posterior error maps, generated throughout
training, to focus sampling on difficult regions, resulting in improved
learning. Our contribution is threefold: 1) We give a detailed description of
the proposed sampling algorithm to speed up and improve learning performance on
large images. We propose a deep dual path CNN that captures information at fine
and coarse scales, resulting in a network with a large field of view and high
resolution outputs. We show that our method is able to attain new
state-of-the-art results on the VISCERAL Anatomy benchmark
BSUV-Net: a fully-convolutional neural network for background subtraction of unseen videos
Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature. In this work, we propose a new, supervised, background subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.Accepted manuscrip
A fully-convolutional neural network for background subtraction of unseen videos
Background subtraction is a basic task in computer vision
and video processing often applied as a pre-processing step
for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have
been proposed, however nearly all of the top-performing
ones are supervised. Crucially, their success relies upon
the availability of some annotated frames of the test video
during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature.
In this work, we propose a new, supervised, backgroundsubtraction algorithm for unseen videos (BSUV-Net) based
on a fully-convolutional neural network. The input to our
network consists of the current frame and two background
frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance
of overfitting, we also introduce a new data-augmentation
technique which mitigates the impact of illumination difference between the background frames and the current frame.
On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of
several metrics including F-measure, recall and precision.Accepted manuscrip
S4Net: Single Stage Salient-Instance Segmentation
We consider an interesting problem-salient instance segmentation in this
paper. Other than producing bounding boxes, our network also outputs
high-quality instance-level segments. Taking into account the
category-independent property of each target, we design a single stage salient
instance segmentation framework, with a novel segmentation branch. Our new
branch regards not only local context inside each detection window but also its
surrounding context, enabling us to distinguish the instances in the same scope
even with obstruction. Our network is end-to-end trainable and runs at a fast
speed (40 fps when processing an image with resolution 320x320). We evaluate
our approach on a publicly available benchmark and show that it outperforms
other alternative solutions. We also provide a thorough analysis of the design
choices to help readers better understand the functions of each part of our
network. The source code can be found at
\url{https://github.com/RuochenFan/S4Net}
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