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Title: Who, Where, and What to Wear? Extracting Fashion Knowledge from Social Media
Authors: Yunshan Ma 
Xun Yang 
Lizi Liao 
Yixin Cao 
Tat-Seng Chua 
Keywords: Fashion Analysis
Fashion Knowledge Extraction
Issue Date: 21-Oct-2019
Citation: Yunshan Ma, Xun Yang, Lizi Liao, Yixin Cao, Tat-Seng Chua (2019-10-21). Who, Where, and What to Wear? Extracting Fashion Knowledge from Social Media. ACM MM 2019 : 257-265. ScholarBank@NUS Repository.
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. © 2019 Association for Computing Machinery.
Source Title: ACM MM 2019
ISBN: 9781450000000
DOI: 10.1145/3343031.3350889
Appears in Collections:Staff Publications

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