{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116182"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116182","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"From pixels to regions: Toward universal image segmentation","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_has_math":false,"creators":["Cheng, Bowen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Schwing, Alexander","Shi, Humphrey","Hasegawa-Johnson, Mark","Darrell, Trevor","Liang, Zhi-Pei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["computer vision","image segmentation","semantic segmentation","instance segmentation","panoptic segmentation"],"languages":["en","eng"],"rights":["Copyright 2022 Bowen Cheng"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116182","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Schwing, Alexander","Shi, Humphrey","Hasegawa-Johnson, Mark","Darrell, Trevor","Liang, Zhi-Pei"]},{"key":"dc:creator","label":"Author","values":["Cheng, Bowen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-06"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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","image segmentation","semantic segmentation","instance segmentation","panoptic segmentation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Bowen Cheng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116182"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Bowen Cheng, accepted the attached license on 2022-07-05 at 10:22.","The student, Bowen Cheng, submitted this Dissertation for approval on 2022-07-05 at 10:28.","This Dissertation was approved for publication on 2022-07-06 at 16:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18142 on 2022-11-15 at 17:38:09","Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task: semantic segmentation is usually formulated as per-pixel classification and mask classification dominates instance-level segmentation tasks. In this dissertation, we demonstrate how to build a single unified architecture that can address any image segmentation task. We first introduce an effort in unifying image segmentation with either per-pixel classification (Panoptic-DeepLab) or mask classification (MaskFormer). We observe mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks. Based on this observation we propose Mask2Former, which outperforms even the best specialized architectures by a significant margin on four popular datasets for three image segmentation tasks (panoptic, instance and semantic). Then we discuss how to evaluate image segmentation models with a new Boundary IoU metric. Finally, we conclude this dissertation with promising future directions to explore."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["From pixels to regions: Toward universal image segmentation"]}]}],"canonical_facts":{"dc:contributor":["Schwing, Alexander","Shi, Humphrey","Hasegawa-Johnson, Mark","Darrell, Trevor","Liang, Zhi-Pei"],"dc:creator":["Cheng, Bowen"],"dc:date":["2022-08","2022-07-06"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Bowen Cheng, accepted the attached license on 2022-07-05 at 10:22.","The student, Bowen Cheng, submitted this Dissertation for approval on 2022-07-05 at 10:28.","This Dissertation was approved for publication on 2022-07-06 at 16:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18142 on 2022-11-15 at 17:38:09","Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task: semantic segmentation is usually formulated as per-pixel classification and mask classification dominates instance-level segmentation tasks. In this dissertation, we demonstrate how to build a single unified architecture that can address any image segmentation task. We first introduce an effort in unifying image segmentation with either per-pixel classification (Panoptic-DeepLab) or mask classification (MaskFormer). We observe mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks. Based on this observation we propose Mask2Former, which outperforms even the best specialized architectures by a significant margin on four popular datasets for three image segmentation tasks (panoptic, instance and semantic). Then we discuss how to evaluate image segmentation models with a new Boundary IoU metric. Finally, we conclude this dissertation with promising future directions to explore."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116182"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Bowen Cheng"],"dc:subject":["computer vision","image segmentation","semantic segmentation","instance segmentation","panoptic segmentation"],"dc:title":["From pixels to regions: Toward universal image segmentation"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}