Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dense Optical Flow Prediction from a Static Image   Given a scene, what is going to move, and in what direction will it move?
Such a question could be considered a non-semantic form of action prediction.
In this work, we present a convolutional neural network (CNN) based approach
for motion prediction. Given a static image, this CNN predicts the future
motion of each and every pixel in the image in terms of optical flow. Our CNN
model leverages the data in tens of thousands of realistic videos to train our
model. Our method relies on absolutely no human labeling and is able to predict
motion based on the context of the scene. Because our CNN model makes no
assumptions about the underlying scene, it can predict future optical flow on a
diverse set of scenarios. We outperform all previous approaches by large
margins.

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The categories of this paper are: math
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph