{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105108"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105108","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enabling type differentiated explainable queries across modalities for different fashion items","abstract":"One of the biggest differences between shopping online and in person is the limited scope and expressibility of the queries that current systems allow and can handle. In person, users often employ a combination of linguistic and visual tools at their disposal to create complex queries. Handling such queries requires modeling relationships between products of the same type, products of different types, products and outfits, and products and their attributes. In this paper, we propose a system that models these relationships by: (i) building a robust visual representation of items that captures notions of similarity and compatibility between products, (ii) learning to predict low-level (color, type) and high-level (style, brand) attributes of the items from their visual representations, and (iii) learning segment-wise maps of outfits to items. For each part, we evaluate the model by demonstrating its performance on relevant tasks like outfit completion, item retrieval, etc., and flexibility through example results for complex queries.","abstract_html":"One of the biggest differences between shopping online and in person is the limited scope and expressibility of the queries that current systems allow and can handle. In person, users often employ a combination of linguistic and visual tools at their disposal to create complex queries. Handling such queries requires modeling relationships between products of the same type, products of different types, products and outfits, and products and their attributes. In this paper, we propose a system that models these relationships by: (i) building a robust visual representation of items that captures notions of similarity and compatibility between products, (ii) learning to predict low-level (color, type) and high-level (style, brand) attributes of the items from their visual representations, and (iii) learning segment-wise maps of outfits to items. For each part, we evaluate the model by demonstrating its performance on relevant tasks like outfit completion, item retrieval, etc., and flexibility through example results for complex queries.","abstract_has_math":false,"creators":["Dusad, Krishna"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Forsyth, David A.","Kumar, Ranjitha"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:36:13Z","date_published":"2019-08-23T20:36:13Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Machine Learning","Artificial Intelligence","Human-Computer Interaction"],"languages":["en"],"rights":["Copyright 2019 Krishna Dusad"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105108","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Forsyth, David A.","Kumar, Ranjitha"]},{"key":"dc:creator","label":"Author","values":["Dusad, Krishna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:36:13Z","2021-08-24T09:15:38Z","2019-04-26","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Artificial Intelligence","Human-Computer Interaction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Krishna Dusad"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105108"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["One of the biggest differences between shopping online and in person is the limited scope and expressibility of the queries that current systems allow and can handle. In person, users often employ a combination of linguistic and visual tools at their disposal to create complex queries. Handling such queries requires modeling relationships between products of the same type, products of different types, products and outfits, and products and their attributes. In this paper, we propose a system that models these relationships by: (i) building a robust visual representation of items that captures notions of similarity and compatibility between products, (ii) learning to predict low-level (color, type) and high-level (style, brand) attributes of the items from their visual representations, and (iii) learning segment-wise maps of outfits to items. For each part, we evaluate the model by demonstrating its performance on relevant tasks like outfit completion, item retrieval, etc., and flexibility through example results for complex queries.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Krishna Dusad, accepted the attached license on 2019-04-26 at 11:30.","The student, Krishna Dusad, submitted this Thesis for approval on 2019-04-26 at 11:39.","This Thesis was approved for publication on 2019-04-26 at 15:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13945 on 2019-08-22 at 15:08:49","Made available in DSpace on 2019-08-23T20:36:13Z (GMT). No. of bitstreams: 2 DUSAD-THESIS-2019.pdf: 17414255 bytes, checksum: 2f966e1c46cf63a5e1f818854856ca87 (MD5) LICENSE.txt: 4210 bytes, checksum: 35e79fa00d134c01166c88270764c0f8 (MD5) Previous issue date: 2019-04-26","Embargo set by: Seth Robbins for item 112227 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112227 on 2021-08-24T09:15:38Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enabling type differentiated explainable queries across modalities for different fashion items"]}]}],"canonical_facts":{"dc:contributor":["Forsyth, David A.","Kumar, Ranjitha"],"dc:creator":["Dusad, Krishna"],"dc:date":["2019-08-23T20:36:13Z","2021-08-24T09:15:38Z","2019-04-26","2019-05"],"dc:description":["One of the biggest differences between shopping online and in person is the limited scope and expressibility of the queries that current systems allow and can handle. In person, users often employ a combination of linguistic and visual tools at their disposal to create complex queries. Handling such queries requires modeling relationships between products of the same type, products of different types, products and outfits, and products and their attributes. In this paper, we propose a system that models these relationships by: (i) building a robust visual representation of items that captures notions of similarity and compatibility between products, (ii) learning to predict low-level (color, type) and high-level (style, brand) attributes of the items from their visual representations, and (iii) learning segment-wise maps of outfits to items. For each part, we evaluate the model by demonstrating its performance on relevant tasks like outfit completion, item retrieval, etc., and flexibility through example results for complex queries.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Krishna Dusad, accepted the attached license on 2019-04-26 at 11:30.","The student, Krishna Dusad, submitted this Thesis for approval on 2019-04-26 at 11:39.","This Thesis was approved for publication on 2019-04-26 at 15:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13945 on 2019-08-22 at 15:08:49","Made available in DSpace on 2019-08-23T20:36:13Z (GMT). No. of bitstreams: 2 DUSAD-THESIS-2019.pdf: 17414255 bytes, checksum: 2f966e1c46cf63a5e1f818854856ca87 (MD5) LICENSE.txt: 4210 bytes, checksum: 35e79fa00d134c01166c88270764c0f8 (MD5) Previous issue date: 2019-04-26","Embargo set by: Seth Robbins for item 112227 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112227 on 2021-08-24T09:15:38Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105108"],"dc:language":["en"],"dc:rights":["Copyright 2019 Krishna Dusad"],"dc:subject":["Machine Learning","Artificial Intelligence","Human-Computer Interaction"],"dc:title":["Enabling type differentiated explainable queries across modalities for different fashion items"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:44Z"}