Understanding the rich world of outfits: a study of fashion compatibility, latent style, and outfit behavior
Vasileva, Mariya Ivanova
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https://hdl.handle.net/2142/108720
Description
Title
Understanding the rich world of outfits: a study of fashion compatibility, latent style, and outfit behavior
Author(s)
Vasileva, Mariya Ivanova
Issue Date
2020-07-22
Director of Research (if dissertation) or Advisor (if thesis)
Forsyth, David A
Doctoral Committee Chair(s)
Forsyth, David A
Committee Member(s)
Hoiem, Derek
Schwing, Alexander
Berg, Tamara L
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2020-10-07T22:50:04Z
Keyword(s)
Computer vision
machine learning applications
explainability
embedding models
vision and language
image search and retrieval
style summarization
fashion compatibility
Abstract
Many computer vision applications in the fashion domain require solving tasks where complex relationships between images, such as the notion of item compatibility, are being learned. We take a metric learning approach to representing compatibility between pairs of items. First, we introduce a model that learns compatibility relationships in dedicated embedding subspaces dependent on item type, which results in significant gains on established fashion compatibility prediction tasks. Second, we present a method for learning a richer notion of compatibility across multiple compatibility conditions whose contributions are learned as a latent variable, which provides better performance on established tasks while requiring fewer embedding subspaces to be learned. Third, we make the first published attempt at diagnosing the salient features of a pair of items that make them compatible, and linking them to human-interpretable concepts. Finally, we demonstrate that our representation of outfits enables diverse, novel, and practically-useful visual search queries for the fashion domain, and results in semantically-meaningful style summaries with several directions for future work.
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