  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.
