Files in this item



application/pdfVASILEVA-DISSERTATION-2020.pdf (100MB)Restricted Access
(no description provided)PDF


Title:Understanding the rich world of outfits: a study of fashion compatibility, latent style, and outfit behavior
Author(s):Vasileva, Mariya Ivanova
Director of Research:Forsyth, David A
Doctoral Committee Chair(s):Forsyth, David A
Doctoral Committee Member(s):Hoiem, Derek; Schwing, Alexander; Berg, Tamara L
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer vision
machine learning applications
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.
Issue Date:2020-07-22
Rights Information:Copyright 2020 Mariya I. Vasileva
Date Available in IDEALS:2020-10-07
Date Deposited:2020-08

This item appears in the following Collection(s)

Item Statistics