Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting

Yunshan Ma; Yujuan Ding; Xun Yang; Lizi Liao; Wai Keung Wong; Tat Seng Chua; Jinyoung Moon; Hong Han Shuai

doi:10.1145/3460426.3463598

Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting

Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat Seng Chua, Jinyoung Moon, Hong Han Shuai

Department of Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval
Publisher	Association for Computing Machinery, Inc
Pages	615-618
Number of pages	4
ISBN (Electronic)	9781450384636
DOIs	https://doi.org/10.1145/3460426.3463598
State	Published - 24 Aug 2021
Event	11th ACM International Conference on Multimedia Retrieval, ICMR 2021 - Taipei, Taiwan Duration: 16 Nov 2021 → 19 Nov 2021

Publication series

Name	ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval

Conference

Conference	11th ACM International Conference on Multimedia Retrieval, ICMR 2021
Country/Territory	Taiwan
City	Taipei
Period	16/11/21 → 19/11/21

Keywords

Fashion analysis
Fashion trend forecasting
Time series forecasting

Access to Document

10.1145/3460426.3463598

Cite this

Ma, Y., Ding, Y., Yang, X., Liao, L., Wong, W. K., Chua, T. S., Moon, J., & Shuai, H. H. (2021). Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting. In ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval (pp. 615-618). (ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval). Association for Computing Machinery, Inc. https://doi.org/10.1145/3460426.3463598

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title = "Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting",

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 analysis, Fashion trend forecasting, Time series forecasting",

author = "Yunshan Ma and Yujuan Ding and Xun Yang and Lizi Liao and Wong, {Wai Keung} and Chua, {Tat Seng} and Jinyoung Moon and Shuai, {Hong Han}",

note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 11th ACM International Conference on Multimedia Retrieval, ICMR 2021 ; Conference date: 16-11-2021 Through 19-11-2021",

year = "2021",

month = aug,

day = "24",

doi = "10.1145/3460426.3463598",

language = "English",

series = "ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval",

publisher = "Association for Computing Machinery, Inc",

pages = "615--618",

booktitle = "ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval",

}

Ma, Y, Ding, Y, Yang, X, Liao, L, Wong, WK, Chua, TS, Moon, J & Shuai, HH 2021, Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting. in ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval. ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval, Association for Computing Machinery, Inc, pp. 615-618, 11th ACM International Conference on Multimedia Retrieval, ICMR 2021, Taipei, Taiwan, 16/11/21. https://doi.org/10.1145/3460426.3463598

Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting. / Ma, Yunshan; Ding, Yujuan; Yang, Xun et al.
ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval. Association for Computing Machinery, Inc, 2021. p. 615-618 (ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ding, Yujuan

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AU - Liao, Lizi

AU - Wong, Wai Keung

AU - Chua, Tat Seng

AU - Moon, Jinyoung

AU - Shuai, Hong Han

PY - 2021/8/24

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N2 - 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.

AB - 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.

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KW - Fashion trend forecasting

KW - Time series forecasting

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Ma Y, Ding Y, Yang X, Liao L, Wong WK, Chua TS et al. Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting. In ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval. Association for Computing Machinery, Inc. 2021. p. 615-618. (ICMR 2021 - Proceedings of the 2021 International Conference on Multimedia Retrieval). doi: 10.1145/3460426.3463598

Reproducibility companion paper: Knowledge enhanced neural fashion trend forecasting

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Keywords

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