Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2019
Abstract
Fashion knowledge plays a pivotal role in helping people in their dressing. In this paper, we present a novel system to automatically harvest fashion knowledge from social media. It unifies three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata. A contextualized fashion concept learning model is applied to leverage the rich contextual information for improving the fashion concept learning performance. At the same time, to counter the label noise within training data, we employ a weak label modeling method to further boost the performance. We build a website to demonstrate the quality of fashion knowledge extracted by our system.
Keywords
Fashion analysis, Fashion knowledge extraction
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering; Intelligent Systems and Optimization
Publication
MM '19: Proceedings of the 27th ACM International Conference on Multimedia
First Page
2223
Last Page
2224
ISBN
9781450368896
Identifier
10.1145/3343031.3350607
Publisher
Association for Computing Machinery
City or Country
New York, NY, United States
Citation
MA, Yunshan; LIAO, Lizi; and CHUA, Tat-Seng.
Automatic fashion knowledge extraction from social media. (2019). MM '19: Proceedings of the 27th ACM International Conference on Multimedia. 2223-2224.
Available at: https://ink.library.smu.edu.sg/sis_research/7721
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/10.1145/3343031.3350607