{"id":"https://openalex.org/W4280652900","doi":"https://doi.org/10.48550/arxiv.2205.04903","title":"Learning Non-target Knowledge for Few-shot Semantic Segmentation","display_name":"Learning Non-target Knowledge for Few-shot Semantic Segmentation","publication_year":2022,"publication_date":"2022-05-10","ids":{"openalex":"https://openalex.org/W4280652900","doi":"https://doi.org/10.48550/arxiv.2205.04903"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2205.04903","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.04903","pdf_url":"https://arxiv.org/pdf/2205.04903","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2205.04903","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076863392","display_name":"Yuanwei Liu","orcid":"https://orcid.org/0000-0002-6389-8941"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yuanwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100377352","display_name":"Nian Liu","orcid":"https://orcid.org/0000-0002-0825-6081"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Nian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084669485","display_name":"Qinglong Cao","orcid":"https://orcid.org/0000-0003-2742-026X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Qinglong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049629716","display_name":"Xiwen Yao","orcid":"https://orcid.org/0000-0002-7466-7428"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Xiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012529382","display_name":"Junwei Han","orcid":"https://orcid.org/0000-0001-5545-7217"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Junwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082634513","display_name":"Ling Shao","orcid":"https://orcid.org/0000-0002-8264-6117"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Ling","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":true,"cited_by_count":6,"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":0.9986000061035156,"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":0.9986000061035156,"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":0.9965000152587891,"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":0.9642999768257141,"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/computer-science","display_name":"Computer science","score":0.8265353441238403},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7778377532958984},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.7684304118156433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5813819766044617},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5733527541160583},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5462354421615601},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43454471230506897},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.42885535955429077},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42154717445373535},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36234739422798157},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3617989122867584},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2607904076576233}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8265353441238403},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7778377532958984},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.7684304118156433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5813819766044617},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5733527541160583},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5462354421615601},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43454471230506897},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.42885535955429077},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42154717445373535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36234739422798157},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3617989122867584},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2607904076576233},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2205.04903","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.04903","pdf_url":"https://arxiv.org/pdf/2205.04903","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2205.04903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2205.04903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"pmh:oai:arXiv.org:2205.04903","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.04903","pdf_url":"https://arxiv.org/pdf/2205.04903","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5706494817","display_name":null,"funder_award_id":"U20B2065","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6164891554","display_name":null,"funder_award_id":"2020AAA0105701","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6671078455","display_name":null,"funder_award_id":"62071388","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8584449773","display_name":null,"funder_award_id":"62136007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G879442631","display_name":null,"funder_award_id":"62036005","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"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4280652900.pdf","grobid_xml":"https://content.openalex.org/works/W4280652900.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4286681602","https://openalex.org/W3209312100","https://openalex.org/W4376620596","https://openalex.org/W3177249605","https://openalex.org/W2534152068","https://openalex.org/W3138508047","https://openalex.org/W1972515067","https://openalex.org/W1689909837","https://openalex.org/W4293054914","https://openalex.org/W4313315626"],"abstract_inverted_index":{"Existing":[0],"studies":[1],"in":[2,23,58],"few-shot":[3],"semantic":[4],"segmentation":[5,100,140],"only":[6,94],"focus":[7],"on":[8,129,164],"mining":[9],"the":[10,59,71,90,96,119,125,152,157],"target":[11,98,138,158],"object":[12,99,139,159],"information,":[13],"however,":[14],"often":[15],"are":[16,113],"hard":[17],"to":[18,49,69,88,115,150],"tell":[19],"ambiguous":[20],"regions,":[21,25],"especially":[22],"non-target":[24],"which":[26,130],"include":[27],"background":[28],"(BG)":[29],"and":[30,52,55,108,121,136,167],"Distracting":[31],"Objects":[32],"(DOs).":[33],"To":[34,80],"alleviate":[35],"this":[36,81],"problem,":[37],"we":[38,83,131,143],"propose":[39,144],"a":[40,62,76,85,104,109,134,145],"novel":[41],"framework,":[42],"namely":[43],"Non-Target":[44],"Region":[45],"Eliminating":[46,106,111],"(NTRE)":[47],"network,":[48],"explicitly":[50],"mine":[51],"eliminate":[53],"BG":[54,63,72,78,86,105,120,135],"DO":[56,110,122],"regions":[57],"query.":[60],"First,":[61],"Mining":[64],"Module":[65,107,112],"(BGMM)":[66],"is":[67,174],"proposed":[68,114],"extract":[70],"region":[73],"via":[74],"learning":[75,91,148],"general":[77],"prototype.":[79],"end,":[82],"design":[84],"loss":[87],"supervise":[89],"of":[92,155],"BGMM":[93],"using":[95],"known":[97],"ground":[101],"truth.":[102],"Then,":[103],"successively":[116],"filter":[117],"out":[118],"information":[123],"from":[124,160],"query":[126],"feature,":[127],"based":[128],"can":[132],"obtain":[133],"DO-free":[137],"result.":[141],"Furthermore,":[142],"prototypical":[146],"contrastive":[147],"algorithm":[149],"improve":[151],"model":[153],"ability":[154],"distinguishing":[156],"DOs.":[161],"Extensive":[162],"experiments":[163],"both":[165],"PASCAL-5i":[166],"COCO-20i":[168],"datasets":[169],"show":[170],"that":[171],"our":[172],"approach":[173],"effective":[175],"despite":[176],"its":[177],"simplicity.":[178]},"counts_by_year":[{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}