Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Occlusion Aware Unsupervised Learning of Optical Flow   It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit the current unsupervised learning of optical flow methods.
In this work we introduce a new method which models occlusion explicitly and a
new warping way that facilitates the learning of large motion. Our method shows
promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
Especially on KITTI dataset where abundant unlabeled samples exist, our
unsupervised method outperforms its counterpart trained with supervised
learning.

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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: physics
34
The categories of this paper are: chem-ph