{"id":"https://openalex.org/W7151494272","doi":"https://doi.org/10.48550/arxiv.2604.03657","title":"Love Me, Love My Label: Rethinking the Role of Labels in Prompt Retrieval for Visual In-Context Learning","display_name":"Love Me, Love My Label: Rethinking the Role of Labels in Prompt Retrieval for Visual In-Context Learning","publication_year":2026,"publication_date":"2026-04-04","ids":{"openalex":"https://openalex.org/W7151494272","doi":"https://doi.org/10.48550/arxiv.2604.03657"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03657","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.03657","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130227048","display_name":"Tianci Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Tianci","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133120124","display_name":"Haohao Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Haohao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133121531","display_name":"Jinpeng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jinpeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133125932","display_name":"Niu Lian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lian, Niu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133092538","display_name":"Xinrui Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xinrui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133132986","display_name":"Bin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133065574","display_name":"Shu-Tao Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Shu-Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133087467","display_name":"Chun Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Chun","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":0.28209999203681946,"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":0.28209999203681946,"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":0.2768000066280365,"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":0.2393999993801117,"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/feature","display_name":"Feature (linguistics)","score":0.5231000185012817},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5033000111579895},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.483599990606308},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.46380001306533813},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.4415999948978424},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41440001130104065},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4133000075817108},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.39410001039505005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7468000054359436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5364000201225281},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5033000111579895},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.483599990606308},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4415999948978424},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41440001130104065},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40389999747276306},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3971000015735626},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.39410001039505005},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.39169999957084656},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C2780277889","wikidata":"https://www.wikidata.org/wiki/Q282301","display_name":"Demonstrative","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C44210515","wikidata":"https://www.wikidata.org/wiki/Q16968978","display_name":"Bespoke","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2989000082015991},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.28279998898506165},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03657","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.03657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03657","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Visual":[0],"in-context":[1,198],"learning":[2],"(VICL)":[3],"enables":[4],"visual":[5],"foundation":[6],"models":[7],"to":[8,118,124,137,148,163],"handle":[9,125],"multiple":[10],"tasks":[11],"by":[12,87],"steering":[13],"them":[14],"with":[15,141,176],"demonstrative":[16],"prompts.":[17],"The":[18],"choice":[19],"of":[20,103,195,217],"such":[21],"prompts":[22,80,117],"largely":[23],"influences":[24],"VICL":[25,68,84,178],"performance,":[26],"standing":[27],"out":[28],"as":[29],"a":[30,92,134,150,177,183],"key":[31],"challenge.":[32],"Prior":[33],"work":[34],"has":[35],"made":[36],"substantial":[37],"progress":[38],"on":[39,48,197],"prompt":[40,49,106,221],"retrieval":[41,222],"and":[42,79,161,174,182,192,201,211],"reranking":[43],"strategies,":[44],"but":[45,62],"mainly":[46],"focuses":[47],"images":[50],"while":[51,154],"overlooking":[52],"labels.":[53],"We":[54,167],"reveal":[55],"these":[56,88],"approaches":[57],"sometimes":[58],"get":[59],"visually":[60],"similar":[61],"label-inconsistent":[63],"prompts,":[64],"which":[65,99],"potentially":[66],"degrade":[67],"performance.":[69],"On":[70],"the":[71,101,138,155,215],"other":[72],"hand,":[73],"higher":[74],"label":[75,120,152,218],"consistency":[76],"between":[77],"query":[78,127],"preferably":[81],"indicates":[82],"stronger":[83],"results.":[85],"Motivated":[86],"findings,":[89],"we":[90,132],"develop":[91],"framework":[93,109],"named":[94],"LaPR":[95,196,205],"(Label-aware":[96],"Prompt":[97],"Retrieval),":[98],"highlights":[100],"role":[102],"labels":[104,128],"in":[105,220],"selection.":[107],"Our":[108],"first":[110],"designs":[111],"an":[112],"image-label":[113],"joint":[114],"representation":[115],"for":[116,172,223],"incorporate":[119],"cues":[121],"explicitly.":[122],"Besides,":[123],"unavailable":[126],"at":[129,228],"test":[130],"time,":[131],"introduce":[133],"mixture-of-expert":[135],"mechanism":[136],"dual":[139],"encoders":[140],"query-adaptive":[142,158],"routing.":[143],"Each":[144],"expert":[145],"is":[146,226],"expected":[147],"capture":[149],"specific":[151],"mode,":[153],"router":[156],"infers":[157],"mixture":[159],"weights":[160],"helps":[162],"learn":[164],"label-aware":[165],"representation.":[166],"carefully":[168],"design":[169],"alternative":[170],"optimization":[171],"experts":[173],"router,":[175],"performance-guided":[179],"contrastive":[180,185],"loss":[181],"label-guided":[184],"loss,":[186],"respectively.":[187],"Extensive":[188],"experiments":[189],"show":[190],"promising":[191],"consistent":[193],"improvement":[194],"segmentation,":[199],"detection,":[200],"colorization":[202],"tasks.":[203],"Moreover,":[204],"generalizes":[206],"well":[207],"across":[208],"feature":[209],"extractors":[210],"cross-fold":[212],"scenarios,":[213],"suggesting":[214],"importance":[216],"utilization":[219],"VICL.":[224],"Code":[225],"available":[227],"https://github.com/luotc-why/CVPR26-LaPR.":[229]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}
