{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108720"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108720","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Understanding the rich world of outfits: a study of fashion compatibility, latent style, and outfit behavior","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Vasileva, Mariya Ivanova"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Forsyth, David A","Hoiem, Derek","Schwing, Alexander","Berg, Tamara L"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-07T22:50:04Z","date_published":"2020-10-07T22:50:04Z","updated_at":"2026-07-22T22:24:48Z","subjects":["Computer vision","machine learning applications","explainability","embedding models","vision and language","image search and retrieval","style summarization","fashion compatibility"],"languages":["en"],"rights":["Copyright 2020 Mariya I. 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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. 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