Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2021
Abstract
This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020. We provide an artifact that allows the replication of the experiments using a Python implementation. The artifact is easy to deploy with simple installation, training and evaluation. We reproduce the experiments conducted in the original paper and obtain similar performance as previously reported. The replication results of the experiments support the main claims in the original paper.
Keywords
Fashion Trend Forecasting, Fashion Analysis, Time Series Forecasting
Discipline
Artificial Intelligence and Robotics | Theory and Algorithms
Publication
ICMR '21: Proceedings of the International Conference on Multimedia Retrieval, August 21-24, Taipei
First Page
615
Last Page
618
ISBN
9781450384636
Identifier
10.1145/3460426.3463598
Publisher
ACM
City or Country
New York
Citation
MA, Yunshan; DING, Yujuan; YANG, Xun; LIAO, Lizi; WONG, Wai Keung; CHUA, Tat-Seng; MOON, Jinyoung; and SHUAI, Hong-Han.
Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting. (2021). ICMR '21: Proceedings of the International Conference on Multimedia Retrieval, August 21-24, Taipei. 615-618.
Available at: https://ink.library.smu.edu.sg/sis_research/7097
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/10.1145/3460426.3463598