Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.de

11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org