Towards Miss Universe Automatic Prediction: The Evening Gown Competition

TL;DR

This paper explores the feasibility of predicting Miss Universe winners from catwalk videos using computer vision, introducing a new dataset and ranking methods, with promising results in identifying top contenders.

Contribution

It introduces the Miss Universe dataset and novel ranking approaches, advancing automatic prediction of winners from visual cues in fashion competitions.

Findings

The proposed system ranks winners in the top 3 for 5 out of 10 competitions.

New video dataset spanning 10 years of Miss Universe competitions.

Effective use of Spatio-temporal features and Fisher Vectors for ranking.

Abstract

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.