Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to encode motion using spatio-temporal synchrony   We consider the task of learning to extract motion from videos. To this end,
we show that the detection of spatial transformations can be viewed as the
detection of synchrony between the image sequence and a sequence of features
undergoing the motion we wish to detect. We show that learning about synchrony
is possible using very fast, local learning rules, by introducing
multiplicative "gating" interactions between hidden units across frames. This
makes it possible to achieve competitive performance in a wide variety of
motion estimation tasks, using a small fraction of the time required to learn
features, and to outperform hand-crafted spatio-temporal features by a large
margin. We also show how learning about synchrony can be viewed as performing
greedy parameter estimation in the well-known motion energy model.

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The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex