{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110748"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110748","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Inferring object states and articulation modes from egocentric videos","abstract":"We develop algorithms for understanding objects from the point of view of interacting with them. There are two key aspects to obtaining such an understanding. First, objects can occur in different states and we need features that are sensitive to such states. Second, different objects can be articulated in different ways and we need to understand how to correctly infer their modes of articulation. We propose self and weakly supervised techniques to obtain such an understanding of objects purely through observation of how humans interact with the world around them through their hands. Our experiments on the challenging EPIC- KITCHENS dataset show the merits of using human hands as a probe for understanding objects.","abstract_html":"We develop algorithms for understanding objects from the point of view of interacting with them. There are two key aspects to obtaining such an understanding. First, objects can occur in different states and we need features that are sensitive to such states. Second, different objects can be articulated in different ways and we need to understand how to correctly infer their modes of articulation. We propose self and weakly supervised techniques to obtain such an understanding of objects purely through observation of how humans interact with the world around them through their hands. Our experiments on the challenging EPIC- KITCHENS dataset show the merits of using human hands as a probe for understanding objects.","abstract_has_math":false,"creators":["Goyal, Rishabh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Gupta, Saurabh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T02:34:49Z","date_published":"2021-09-17T02:34:49Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Computer Vision","Machine Learning","Feature Learning","Object Understanding"],"languages":["en"],"rights":["Copyright 2021 Rishabh Goyal"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110748","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gupta, Saurabh"]},{"key":"dc:creator","label":"Author","values":["Goyal, Rishabh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:49Z","2023-09-17T02:34:57Z","2021-04-28","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Computer Vision","Machine Learning","Feature Learning","Object Understanding"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Rishabh Goyal"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110748"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We develop algorithms for understanding objects from the point of view of interacting with them. 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Our experiments on the challenging EPIC- KITCHENS dataset show the merits of using human hands as a probe for understanding objects.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Rishabh Goyal, accepted the attached license on 2021-04-27 at 12:26.","The student, Rishabh Goyal, submitted this Thesis for approval on 2021-04-27 at 13:39.","This Thesis was approved for publication on 2021-04-28 at 09:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16585 on 2021-09-16 at 17:06:08","Made available in DSpace on 2021-09-17T02:34:49Z (GMT). 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There are two key aspects to obtaining such an understanding. First, objects can occur in different states and we need features that are sensitive to such states. Second, different objects can be articulated in different ways and we need to understand how to correctly infer their modes of articulation. We propose self and weakly supervised techniques to obtain such an understanding of objects purely through observation of how humans interact with the world around them through their hands. Our experiments on the challenging EPIC- KITCHENS dataset show the merits of using human hands as a probe for understanding objects.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Rishabh Goyal, accepted the attached license on 2021-04-27 at 12:26.","The student, Rishabh Goyal, submitted this Thesis for approval on 2021-04-27 at 13:39.","This Thesis was approved for publication on 2021-04-28 at 09:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16585 on 2021-09-16 at 17:06:08","Made available in DSpace on 2021-09-17T02:34:49Z (GMT). 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