Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow   Optical flow estimation can be formulated as an end-to-end supervised
learning problem, which yields estimates with a superior accuracy-runtime
tradeoff compared to alternative methodology. In this paper, we make such
networks estimate their local uncertainty about the correctness of their
prediction, which is vital information when building decisions on top of the
estimations. For the first time we compare several strategies and techniques to
estimate uncertainty in a large-scale computer vision task like optical flow
estimation. Moreover, we introduce a new network architecture utilizing the
Winner-Takes-All loss and show that this can provide complementary hypotheses
and uncertainty estimates efficiently with a single forward pass and without
the need for sampling or ensembles. Finally, we demonstrate the quality of the
different uncertainty estimates, which is clearly above previous confidence
measures on optical flow and allows for interactive frame rates.

27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an