Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Uncertain Future: Forecasting from Static Images using Variational
  Autoencoders   In a given scene, humans can often easily predict a set of immediate future
events that might happen. However, generalized pixel-level anticipation in
computer vision systems is difficult because machine learning struggles with
the ambiguity inherent in predicting the future. In this paper, we focus on
predicting the dense trajectory of pixels in a scene, specifically what will
move in the scene, where it will travel, and how it will deform over the course
of one second. We propose a conditional variational autoencoder as a solution
to this problem. In this framework, direct inference from the image shapes the
distribution of possible trajectories, while latent variables encode any
necessary information that is not available in the image. We show that our
method is able to successfully predict events in a wide variety of scenes and
can produce multiple different predictions when the future is ambiguous. Our
algorithm is trained on thousands of diverse, realistic videos and requires
absolutely no human labeling. In addition to non-semantic action prediction, we
find that our method learns a representation that is applicable to semantic
vision tasks.

14
The categories of this paper are: math-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
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The categories of this paper are: dg-ga