Can AI decrypt fashion jargon for you?
This addresses the challenge for consumers and fashion enthusiasts in interpreting domain-specific jargon on fashion websites, though it is incremental as it applies existing deep learning methods to a new domain.
The paper tackled the problem of understanding high-level fashion concepts from low-level product descriptions by proposing a data-driven solution that uses a deep learning model to predict and explain these concepts from product images and features, based on a new dataset of 853,056 products and 1,546 categorized keywords.
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.