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.
[1]
Thomas Brox,et al.
U-Net: Convolutional Networks for Biomedical Image Segmentation
,
2015,
MICCAI.
[2]
Zunlei Feng,et al.
Interpretable Partitioned Embedding for Customized Multi-item Fashion Outfit Composition
,
2018,
ICMR.
[3]
S. Maier,et al.
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 227 The Simplicity Principle in Human Concept Learning
,
2022
.
[4]
Carlos Guestrin,et al.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
,
2016,
ArXiv.
[5]
Arvind Satyanarayan,et al.
The Building Blocks of Interpretability
,
2018
.