Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comparing Correspondences: Video Prediction with Correspondence-wise
  Losses   Image prediction methods often struggle on tasks that require changing the
positions of objects, such as video prediction, producing blurry images that
average over the many positions that objects might occupy. In this paper, we
propose a simple change to existing image similarity metrics that makes them
more robust to positional errors: we match the images using optical flow, then
measure the visual similarity of corresponding pixels. This change leads to
crisper and more perceptually accurate predictions, and does not require
modifications to the image prediction network. We apply our method to a variety
of video prediction tasks, where it obtains strong performance with simple
network architectures, and to the closely related task of video interpolation.
Code and results are available at our webpage:
https://dangeng.github.io/CorrWiseLosses

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The categories of this paper are: stat
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The categories of this paper are: econ
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph