{"id":"https://openalex.org/W4377865048","doi":"https://doi.org/10.48550/arxiv.2305.13031","title":"HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation","display_name":"HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation","publication_year":2023,"publication_date":"2023-05-22","ids":{"openalex":"https://openalex.org/W4377865048","doi":"https://doi.org/10.48550/arxiv.2305.13031"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2305.13031","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.13031","pdf_url":"https://arxiv.org/pdf/2305.13031","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.13031","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100667253","display_name":"Jian Ding","orcid":"https://orcid.org/0000-0001-9573-1049"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ding, Jian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038384679","display_name":"Nan Xue","orcid":"https://orcid.org/0000-0002-4729-0741"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Nan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073032922","display_name":"Gui-Song Xia","orcid":"https://orcid.org/0000-0001-7660-6090"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Gui-Song","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051534545","display_name":"Bernt Schiele","orcid":"https://orcid.org/0000-0001-9683-5237"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schiele, Bernt","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5078838951","display_name":"Dengxin Dai","orcid":"https://orcid.org/0000-0001-5440-9678"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Dengxin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100667253"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9459999799728394,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7988531589508057},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7728155851364136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6279081702232361},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6235008835792542},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5892104506492615},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5064877271652222},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47629156708717346},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35439926385879517}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7988531589508057},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7728155851364136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6279081702232361},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6235008835792542},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5892104506492615},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5064877271652222},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47629156708717346},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35439926385879517},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2305.13031","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.13031","pdf_url":"https://arxiv.org/pdf/2305.13031","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2305.13031","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.13031","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2305.13031","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.13031","pdf_url":"https://arxiv.org/pdf/2305.13031","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1186883693","display_name":null,"funder_award_id":"U22B201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2597775472","display_name":null,"funder_award_id":"21013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2852671763","display_name":null,"funder_award_id":"62101390","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3965009956","display_name":null,"funder_award_id":"U22B2011","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4377865048.pdf","grobid_xml":"https://content.openalex.org/works/W4377865048.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W1647606319","https://openalex.org/W4390494008","https://openalex.org/W2053596378","https://openalex.org/W2922442631","https://openalex.org/W2168523118","https://openalex.org/W2073639911","https://openalex.org/W2043988397"],"abstract_inverted_index":{"Current":[0],"semantic":[1,45,154,164],"segmentation":[2,46,155,165],"models":[3],"have":[4],"achieved":[5],"great":[6],"success":[7],"under":[8,47],"the":[9,48,59,65,87],"independent":[10],"and":[11,62,80,112,127,171,175],"identically":[12],"distributed":[13],"(i.i.d.)":[14],"condition.":[15],"However,":[16],"in":[17],"real-world":[18],"applications,":[19],"test":[20],"data":[21],"might":[22],"come":[23],"from":[24],"a":[25,53,98,128],"different":[26,119],"domain":[27,40,49,61],"than":[28,78,167],"training":[29],"data.":[30],"Therefore,":[31],"it":[32],"is":[33,55,84],"important":[34],"to":[35,86,104,108,122],"improve":[36],"model":[37,54],"robustness":[38],"against":[39],"differences.":[41],"This":[42],"work":[43],"studies":[44],"generalization":[50],"setting,":[51],"where":[52],"trained":[56],"only":[57],"on":[58,64],"source":[60],"tested":[63],"unseen":[66],"target":[67],"domain.":[68],"Existing":[69],"works":[70],"show":[71,81,158],"that":[72,82,159],"Vision":[73],"Transformers":[74],"are":[75],"more":[76,162],"robust":[77,163],"CNNs":[79],"this":[83,94],"related":[85],"visual":[88],"grouping":[89,101,173],"property":[90],"of":[91,130],"self-attention.":[92],"In":[93],"work,":[95],"we":[96],"propose":[97],"novel":[99],"hierarchical":[100],"transformer":[102],"(HGFormer)":[103],"explicitly":[105],"group":[106],"pixels":[107],"form":[109],"part-level":[110],"masks":[111,117],"then":[113],"whole-level":[114],"masks.":[115],"The":[116],"at":[118,137,184],"scales":[120,139],"aim":[121],"segment":[123],"out":[124],"both":[125,138],"parts":[126],"whole":[129],"classes.":[131],"HGFormer":[132,160],"combines":[133],"mask":[134],"classification":[135,169],"results":[136,166],"for":[140],"class":[141],"label":[142],"prediction.":[143],"We":[144],"assemble":[145],"multiple":[146],"interesting":[147],"cross-domain":[148],"settings":[149],"by":[150],"using":[151],"seven":[152],"public":[153],"datasets.":[156],"Experiments":[157],"yields":[161],"per-pixel":[168],"methods":[170,178],"flat":[172],"transformers,":[174],"outperforms":[176],"previous":[177],"significantly.":[179],"Code":[180],"will":[181],"be":[182],"available":[183],"https://github.com/dingjiansw101/HGFormer.":[185]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2023-05-24T00:00:00"}
