{"id":"https://openalex.org/W7147144355","doi":"https://doi.org/10.48550/arxiv.2603.26775","title":"Learning to Select Visual In-Context Demonstrations","display_name":"Learning to Select Visual In-Context Demonstrations","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7147144355","doi":"https://doi.org/10.48550/arxiv.2603.26775"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26775","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26775","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.26775","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101765015","display_name":"Eugene Lee","orcid":"https://orcid.org/0000-0001-9967-1186"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lee, Eugene","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132624234","display_name":"Yu-Chi Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Yu-Chi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132699443","display_name":"Jiajie Diao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diao, Jiajie","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101765015"],"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9383000135421753,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9383000135421753,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.040300000458955765,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.0027000000700354576,"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/selection","display_name":"Selection (genetic algorithm)","score":0.5946000218391418},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5885000228881836},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5422000288963318},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5421000123023987},{"id":"https://openalex.org/keywords/preference-learning","display_name":"Preference learning","score":0.39309999346733093},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.39250001311302185},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3822999894618988},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.3714999854564667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7663000226020813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6636000275611877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6036999821662903},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5946000218391418},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5885000228881836},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5422000288963318},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5421000123023987},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.39309999346733093},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3822999894618988},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C187029079","wikidata":"https://www.wikidata.org/wiki/Q958679","display_name":"Cognitive reframing","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.352400004863739},{"id":"https://openalex.org/C2779321571","wikidata":"https://www.wikidata.org/wiki/Q7936605","display_name":"Visual learning","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.33239999413490295},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2628999948501587}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26775","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26775","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":"doi:10.48550/arxiv.2603.26775","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26775","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.8021152019500732}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"Large":[1],"Language":[2],"Models":[3],"(MLLMs)":[4],"adapt":[5],"to":[6,48,66,75],"visual":[7,102,129,146],"tasks":[8],"via":[9],"in-context":[10],"learning":[11],"(ICL),":[12],"which":[13],"relies":[14],"heavily":[15],"on":[16,122],"demonstration":[17,21,78],"quality.":[18],"The":[19],"dominant":[20],"selection":[22,57,141],"strategy":[23],"is":[24,35,142],"unsupervised":[25],"k-Nearest":[26],"Neighbor":[27],"(kNN)":[28],"search.":[29],"While":[30],"simple,":[31],"this":[32],"similarity-first":[33],"approach":[34],"sub-optimal":[36],"for":[37,114,145],"complex":[38],"factual":[39,124],"regression":[40,103,125,136],"tasks;":[41],"it":[42],"selects":[43],"redundant":[44],"examples":[45],"that":[46,94],"fail":[47],"capture":[49],"the":[50],"task's":[51],"full":[52],"output":[53],"range.":[54],"We":[55],"reframe":[56],"as":[58],"a":[59,71,81,85,92,107],"sequential":[60],"decision-making":[61],"problem":[62],"and":[63],"introduce":[64],"Learning":[65,73],"Select":[67],"Demonstrations":[68],"(LSD),":[69],"training":[70],"Reinforcement":[72],"agent":[74,90],"construct":[76],"optimal":[77,113],"sets.":[79],"Using":[80],"Dueling":[82],"DQN":[83],"with":[84,131],"query-centric":[86],"Transformer":[87],"Decoder,":[88],"our":[89],"learns":[91],"policy":[93],"maximizes":[95],"MLLM":[96],"downstream":[97],"performance.":[98],"Evaluating":[99],"across":[100],"five":[101],"benchmarks,":[104],"we":[105],"uncover":[106],"crucial":[108],"dichotomy:":[109],"while":[110],"kNN":[111],"remains":[112],"subjective":[115],"preference":[116],"tasks,":[117],"LSD":[118,133],"significantly":[119],"outperforms":[120],"baselines":[121],"objective,":[123],"tasks.":[126],"By":[127],"balancing":[128],"relevance":[130],"diversity,":[132],"better":[134],"defines":[135],"boundaries,":[137],"illuminating":[138],"when":[139],"learned":[140],"strictly":[143],"necessary":[144],"ICL.":[147]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}
