Interpretable partitioned embedding for customized multi-item fashion outfit composition

Zunlei Feng, Zhenyun Yu, Yezhou Yang, Yongcheng Jing, Junxiao Jiang, Mingli Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Intelligent fashion outfit composition becomes more and more popular in these years. Some deep learning based approaches reveal competitive composition recently. However, the uninterpretable characteristic makes such deep learning based approach cannot meet the designers, businesses and consumers' urge to comprehend the importance of different attributes in an outfit composition. To realize interpretable and customized multi-item fashion outfit compositions, we propose a partitioned embedding network to learn interpretable embeddings from clothing items. The network consists of two vital components: attribute partition module and partition adversarial module. In the attribute partition module, multiple attribute labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the partition adversarial module, adversarial operations are adopted to achieve the independence of different parts. With the interpretable and partitioned embedding, we then construct an outfit composition graph and an attribute matching map. Extensive experiments demonstrate that 1) the partitioned embedding have unmingled parts which corresponding to different attributes and 2) outfits recommended by our model are more desirable in comparison with the existing methods.

Original languageEnglish (US)
Title of host publicationICMR 2018 - Proceedings of the 2018 ACM International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages143-151
Number of pages9
ISBN (Print)9781450350464
DOIs
StatePublished - Jun 5 2018
Event8th ACM International Conference on Multimedia Retrieval, ICMR 2018 - Yokohama, Japan
Duration: Jun 11 2018Jun 14 2018

Other

Other8th ACM International Conference on Multimedia Retrieval, ICMR 2018
CountryJapan
CityYokohama
Period6/11/186/14/18

Keywords

  • Adversarial
  • Embedding
  • Interpretable
  • Outfit composition

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Computer Science Applications

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  • Cite this

    Feng, Z., Yu, Z., Yang, Y., Jing, Y., Jiang, J., & Song, M. (2018). Interpretable partitioned embedding for customized multi-item fashion outfit composition. In ICMR 2018 - Proceedings of the 2018 ACM International Conference on Multimedia Retrieval (pp. 143-151). Association for Computing Machinery, Inc. https://doi.org/10.1145/3206025.3206048