Related papers: Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste

Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste

URL: http://arxiv.org/abs/2305.02307v1
Date: Wed, 3 May 2023 17:54:50 GMT
Title: Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste
Authors: Mengyun Shi, Serge Belongie, Claire Cardie
Abstract summary: We introduce an interpretability dataset, Fashionpedia-taste, to explain why a subject like or dislike a fashion image. 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.
Score: 30.633812626305552
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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