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.

翻译：现有时尚数据集未考虑导致消费者喜欢或不喜欢某张时尚图片的多重因素。即使两位消费者喜欢同一张时尚图片，他们也可能基于完全不同的理由产生偏好。本文研究消费者为何喜欢某张特定时尚图片的原因。为此，我们引入了一个可解释性数据集Fashionpedia-taste，其中包含丰富的标注，从以下三个角度解释受试者喜欢或不喜欢某张时尚图片的原因：1）局部属性；2）人类注意力；3）描述文本。此外，受试者还需提供个人属性及时尚偏好信息（如性格特征和偏好的时尚品牌）。本数据集使研究者能够构建计算模型，从不同人文学科视角和模态全面理解并解释人类时尚品味。