Publication
Neuroaesthetics in fashion: Modeling the perception of fashionability
Conference Article
Conference
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Edition
2015
Doc link
http://dx.doi.org/10.1109/CVPR.2015.7298688
File
Abstract
In this paper, we analyze the fashion of clothing of a large social website. Our goal is to learn and predict how fashionable a person looks on a photograph and suggest subtle improvements the user could make to improve her/his appeal. We propose a Conditional Random Field model that jointly reasons about several fashionability factors such as the type of outfit and garments the user is wearing, the type of the user, the photograph’s setting (e.g., the scenery behind the user), and the fashionability score. Importantly, our model is able to give rich feedback back to the user, conveying which garments or even scenery she/he should change in order to improve fashionability. We demonstrate that our joint approach significantly outperforms a variety of intelligent baselines. We additionally collected a novel heterogeneous dataset with 144,169 user posts containing diverse image, textual and meta information which can be exploited for our task. We also provide a detailed analysis of the data, showing different outfit trends and fashionability scores across the globe and across a span of 6 years.
Categories
computer vision.
Author keywords
fashionability, CRF, deep network, fashion, computer vision, machine learning
Scientific reference
E. Simo-Serra, S. Fidler, F. Moreno-Noguer and R. Urtasun. Neuroaesthetics in fashion: Modeling the perception of fashionability, 2015 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2015, Boston, MA, USA.
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