Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2019

Abstract

Fashion knowledge helps people to dress properly and addresses not only physiological needs of users, but also the demands of social activities and conventions. It usually involves three mutually related aspects of: occasion, person and clothing. However, there are few works focusing on extracting such knowledge, which will greatly benefit many downstream applications, such as fashion recommendation. In this paper, we propose a novel method to automatically harvest fashion knowledge from social media. We unify three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata. For person detection and analysis, we use the off-the-shelf tools due to their flexibility and satisfactory performance. For clothing recognition and occasion prediction, we unify the two tasks by using a contextualized fashion concept learning module, which captures the dependencies and correlations among different fashion concepts. To alleviate the heavy burden of human annotations, we introduce a weak label modeling module which can effectively exploit machine-labeled data, a complementary of clean data. In experiments, we contribute a benchmark dataset and conduct extensive experiments from both quantitative and qualitative perspectives. The results demonstrate the effectiveness of our model in fashion concept prediction, and the usefulness of extracted knowledge with comprehensive analysis.

Keywords

Fashion analysis, Fashion knowledge extraction

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Cybersecurity; Intelligent Systems and Optimization

Publication

MM '19: Proceedings of the 27th ACM International Conference on Multimedia

First Page

257

Last Page

265

ISBN

9781450368896

Identifier

10.1145/3343031.3350889

Publisher

Association for Computing Machinery

City or Country

New York, NY, United States

Additional URL

https://doi.org/10.1145/3343031.3350889

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