Learning Personal Tastes in Choosing Fashion Outfits

Yusan Lin, Maryam Moosaei, Hao Yang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


With the emergence of fashion recommendation, many researchers have attempted to recommend fashion items that fit consumers' tastes. However, few have looked into fashion outfits as a whole when making recommendations. In this paper, we propose a neural network that learns one's fashion taste and predicts whether an individual likes a fashion outfit. To improve learning, we also develop a fashion outfit negative sampling scheme to sample fashion outfits that are different enough. With experiments on the collected Polyvore dataset, we find that using complete images of fashion outfits performs well when learning individuals' tastes toward fashion outfits. Our proposed negative sampling scheme also improves the model's performance significantly, compared to random negative sampling.

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[bibtex]
@InProceedings{Lin_2019_CVPR_Workshops,
author = {Lin, Yusan and Moosaei, Maryam and Yang, Hao},
title = {Learning Personal Tastes in Choosing Fashion Outfits},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}