Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste
This addresses the need for interpretable fashion datasets to explain consumer preferences, though it is incremental as it builds on existing fashion data by adding multi-factored explanations.
The paper tackles the problem of understanding why consumers like or dislike fashion images by introducing Fashionpedia-Taste, a dataset with rich annotations from localized attributes, human attention, and captions, along with personal attributes, enabling computational models to interpret human fashion taste.
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.