Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent Fully Convolutional Networks for Video Segmentation   Image segmentation is an important step in most visual tasks. While
convolutional neural networks have shown to perform well on single image
segmentation, to our knowledge, no study has been been done on leveraging
recurrent gated architectures for video segmentation. Accordingly, we propose a
novel method for online segmentation of video sequences that incorporates
temporal data. The network is built from fully convolutional element and
recurrent unit that works on a sliding window over the temporal data. We also
introduce a novel convolutional gated recurrent unit that preserves the spatial
information and reduces the parameters learned. Our method has the advantage
that it can work in an online fashion instead of operating over the whole input
batch of video frames. The network is tested on the change detection dataset,
and proved to have 5.5\% improvement in F-measure over a plain fully
convolutional network for per frame segmentation. It was also shown to have
improvement of 1.4\% for the F-measure compared to our baseline network that we
call FCN 12s.

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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: hep-ex