2020/03/18 by Yuan Shen, Shanduojiao Jiang, Shen, Yuan +5
Arts and Humanities · Computer Science · Neuroscience · #Aesthetic Perception and Analysis #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fashion and Cultural Textiles #Generative Adversarial Networks and Image Synthesis #Human-Computer Interaction (cs.HC) #I.4.10 #I.4.9 #Information Retrieval (cs.IR) #cs.CV #cs.HC #cs.IR
paper · pdf · doi:10.48550/arxiv.2003.08052
5 pages, 6 figures, Accepted at workshop paper for AI4HCI at CHI2020
arxiv created 2020/03/18 · openalex publication_date 2020/03/18 · arxiv updated 2020/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.