Neuroaesthetics in fashion: Modeling the perception of fashionability

By Edgar Simo-Serra, Sanja Fidler, Francesc Moreno-Noguer, Raquel Urtasun. 2015

227 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Cognitive Science & Psychology

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Citations in the corpus

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, background): “For instance, the work of [32] can tell a user how to become more fashionable after taking a look at a photograph with the user in it.”
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, background): “For example, Simo-Serra et al. (2014) predicts the fashionability of a person in a photograph and suggest subtle improvements.”
Instance-level Human Parsing via Part Grouping Network (2018, background): “…exist a few datasets in the vision community that were dedicated to other tasks, e.g ., clothes recognition, retrieval [28,30] and fashion modeling [36], our CIHP that mainly focuses on instance-level human parsing is the largest one and provides more elaborate dense annotations for diverse images.”
Automatic Spatially-Aware Fashion Concept Discovery (2017, background): “There have been several clothing datasets collected recently [16, 8, 21, 6, 7].”
Learning Fashion Compatibility with Bidirectional LSTMs (2017, background): “On the other hand, some recent works [8, 21] attempted to predict the popularity or “fashionability” of an outfit, but they fail to handle the outfit generation task.”
Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark (2018, methodology): “the tasks of clothes recognition, retrieval [39] and fashion modeling [40], whereas our LIP dataset only focuses on”
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback (2021, background): “Many fashion datasets have been proposed over the past few years, covering different applications such as fashionability and style prediction [50, 28, 22, 51], fashion image generation [46], product search and recommendation [25, 72, 19, 41, 63], fashion apparel pixelwise segmentation [27, 74, 69], ”