  Can we predict the winner of Miss Universe after watching how they stride
down the catwalk during the evening gown competition? Fashion gurus say they
can! In our work, we study this question from the perspective of computer
vision. In particular, we want to understand whether existing computer vision
approaches can be used to automatically extract the qualities exhibited by the
Miss Universe winners during their catwalk. This study can pave the way towards
new vision-based applications for the fashion industry. To this end, we propose
a novel video dataset, called the Miss Universe dataset, comprising 10 years of
the evening gown competition selected between 1996-2010. We further propose two
ranking-related problems: (1) Miss Universe Listwise Ranking and (2) Miss
Universe Pairwise Ranking. In addition, we also develop an approach that
simultaneously addresses the two proposed problems. To describe the videos we
employ the recently proposed Stacked Fisher Vectors in conjunction with robust
local spatio-temporal features. From our evaluation we found that although the
addressed problems are extremely challenging, the proposed system is able to
rank the winner in the top 3 best predicted scores for 5 out of 10 Miss
Universe competitions.
