{"id":"https://openalex.org/W7165807291","doi":"https://doi.org/10.48550/arxiv.2606.24296","title":"Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation","display_name":"Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165807291","doi":"https://doi.org/10.48550/arxiv.2606.24296"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24296","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.24296","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100802179","display_name":"Sujun Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Sujun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102968758","display_name":"Mingwu Ren","orcid":"https://orcid.org/0000-0001-5576-3281"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Mingwu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139238125","display_name":"Haofeng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haofeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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":0.9603000283241272,"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":0.9603000283241272,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.017999999225139618,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0026000000070780516,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7315000295639038},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6959999799728394},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.6474999785423279},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5526000261306763},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.544700026512146},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5299999713897705},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.48420000076293945},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.47699999809265137},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4740999937057495}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7651000022888184},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7315000295639038},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6959999799728394},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.6474999785423279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5559999942779541},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5526000261306763},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.544700026512146},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5299999713897705},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.48420000076293945},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.47699999809265137},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4311000108718872},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2870999872684479},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26460000872612},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2615000009536743}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24296","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.24296","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24296","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7538750767707825,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cross-domain":[0],"Few-shot":[1],"Segmentation":[2],"(CD-FSS)":[3],"aims":[4],"to":[5,62,112,132,166],"learn":[6],"generalizable":[7],"segmentation":[8,20],"capability":[9],"from":[10,153],"abundant":[11],"annotated":[12,32],"samples":[13,159],"in":[14,24,52,66],"the":[15,25,79,91,105,117,125,139],"source":[16],"domain,":[17],"enabling":[18],"accurate":[19],"of":[21,101],"novel":[22],"classes":[23],"target":[26],"domain":[27],"with":[28],"only":[29],"a":[30],"few":[31],"samples.":[33],"Existing":[34],"CD-FSS":[35],"methods":[36],"mainly":[37],"focus":[38],"on":[39,172],"mitigating":[40],"feature":[41],"distribution":[42],"shifts":[43],"caused":[44],"by":[45],"style":[46],"gaps":[47],"while":[48],"ignoring":[49],"significant":[50],"differences":[51],"class":[53,150],"semantic":[54,69,114],"granularity":[55],"and":[56,71,148,158],"discriminative":[57],"attributes":[58],"across":[59],"domains,":[60],"leading":[61],"two":[63],"key":[64,88],"degradations":[65],"support-query":[67],"matching:":[68],"over-alignment":[70],"attribute":[72,122,134],"over-alignment.":[73,115,135],"To":[74],"this":[75],"end,":[76],"we":[77,137],"propose":[78,138],"Dual":[80],"Hierarchical":[81,92],"Aggregation":[82,94],"Network":[83],"(DHANet),":[84],"which":[85,145],"comprises":[86],"three":[87],"modules.":[89],"First,":[90],"Spatial":[93],"(HSA)":[95],"module":[96,119],"performs":[97],"multi-scale":[98,121],"region":[99],"aggregation":[100,123],"pixel":[102],"features":[103,111,131],"along":[104,124],"spatial":[106],"dimension,":[107,127],"generating":[108,128],"hierarchical":[109,129],"semantic-enhanced":[110],"alleviate":[113],"Additionally,":[116],"HCA":[118],"conducts":[120],"channel":[126],"attribute-enhanced":[130],"mitigate":[133,167],"Finally,":[136],"Online":[140],"Probabilistic":[141],"Semantic":[142],"Bank":[143],"(OPSB),":[144],"progressively":[146],"constructs":[147],"updates":[149],"probability":[151],"distributions":[152],"query":[154],"predictions":[155],"during":[156],"inference,":[157],"multiple":[160],"pseudo-prototypes":[161],"as":[162],"additional":[163],"support":[164],"information":[165],"insufficient":[168],"support.":[169],"Extensive":[170],"experiments":[171],"four":[173],"target-domain":[174],"datasets":[175],"demonstrate":[176],"that":[177],"our":[178],"method":[179],"achieves":[180],"state-of-the-art":[181],"performance.":[182]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-25T00:00:00"}
