  When people talk about fashion, they care about the underlying meaning of
fashion concepts,e.g., style.For example, people ask questions like what
features make this dress smart.However, the product descriptions in today
fashion websites are full of domain specific and low level words. It is not
clear to people how exactly those low level descriptions can contribute to a
style or any high level fashion concept. In this paper, we proposed a data
driven solution to address this concept understanding issues by leveraging a
large number of existing product data on fashion sites. We first collected and
categorized 1546 fashion keywords into 5 different fashion categories. Then, we
collected a new fashion product dataset with 853,056 products in total.
Finally, we trained a deep learning model that can explicitly predict and
explain high level fashion concepts in a product image with its low level and
domain specific fashion features.
