Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2020
Abstract
Fashion trend forecasting is a crucial task for both academia andindustry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal thereal fashion trends. Towards insightful fashion trend forecasting,this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge Enhanced Recurrent Network model (KERN) which takes advantage of the capability of deep recurrent neural networks in modeling time series data. Moreover, it leverages internal and external knowledgein fashion domain that affects the time-series patterns of fashion element trends. Such incorporation of domain knowledge further enhances the deep learning model in capturing the patterns of specific fashion elements and predicting the future trends. Extensive experiments demonstrate that the proposed KERN model can effectively capture the complicated patterns of objective fashion elements, therefore making preferable fashion trend forecast.
Keywords
Fashion analysis, Fashion trend forecasting, Time series forecasting
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering; Intelligent Systems and Optimization
Publication
ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval
First Page
82
Last Page
90
ISBN
9781450370875
Identifier
10.1145/3372278.3390677
Publisher
Association for Computing Machinery
City or Country
New York, NY, United States
Citation
MA, Yunshan; DING, Yujuan; YANG, Xun; LIAO, Lizi; WONG, Wai Keung; and CHUA, Tat-Seng.
Knowledge enhanced neural fashion trend forecasting. (2020). ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval. 82-90.
Available at: https://ink.library.smu.edu.sg/sis_research/7674
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3372278.3390677