Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste
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Fashionpedia-Taste : A Dataset towards Explaining Human Fashion Taste. / Shi, Mengyun; Belongie, Serge; Cardie, Claire.
arXiv.org, 2023.Research output: Working paper › Preprint › Research
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TY - UNPB
T1 - Fashionpedia-Taste
T2 - A Dataset towards Explaining Human Fashion Taste
AU - Shi, Mengyun
AU - Belongie, Serge
AU - Cardie, Claire
PY - 2023
Y1 - 2023
N2 - Existing fashion datasets do not consider the multi-facts that cause a consumer to like or dislike a fashion image. Even two consumers like a same fashion image, they could like this image for total different reasons. In this paper, we study the reason why a consumer like a certain fashion image. Towards this goal, we introduce an interpretability dataset, Fashionpedia-taste, consist of rich annotation to explain why a subject like or dislike a fashion image from the following 3 perspectives: 1) localized attributes; 2) human attention; 3) caption. Furthermore, subjects are asked to provide their personal attributes and preference on fashion, such as personality and preferred fashion brands. Our dataset makes it possible for researchers to build computational models to fully understand and interpret human fashion taste from different humanistic perspectives and modalities.
AB - Existing fashion datasets do not consider the multi-facts that cause a consumer to like or dislike a fashion image. Even two consumers like a same fashion image, they could like this image for total different reasons. In this paper, we study the reason why a consumer like a certain fashion image. Towards this goal, we introduce an interpretability dataset, Fashionpedia-taste, consist of rich annotation to explain why a subject like or dislike a fashion image from the following 3 perspectives: 1) localized attributes; 2) human attention; 3) caption. Furthermore, subjects are asked to provide their personal attributes and preference on fashion, such as personality and preferred fashion brands. Our dataset makes it possible for researchers to build computational models to fully understand and interpret human fashion taste from different humanistic perspectives and modalities.
M3 - Preprint
BT - Fashionpedia-Taste
PB - arXiv.org
ER -
ID: 384658533