{"id":"https://openalex.org/W4417268937","doi":"https://doi.org/10.48550/arxiv.2507.20028","title":"TAPS : Frustratingly Simple Test Time Active Learning for VLMs","display_name":"TAPS : Frustratingly Simple Test Time Active Learning for VLMs","publication_year":2025,"publication_date":"2025-07-26","ids":{"openalex":"https://openalex.org/W4417268937","doi":"https://doi.org/10.48550/arxiv.2507.20028"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2507.20028","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.20028","pdf_url":"https://arxiv.org/pdf/2507.20028","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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/2507.20028","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sarkar, Dhruv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarkar, Dhruv","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116078161","display_name":"Aprameyo Chakrabartty","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chakrabartty, Aprameyo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5120782704","display_name":"Bibhudatta Bhanja","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhanja, Bibhudatta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.3970000147819519,"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":0.3970000147819519,"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":0.17949999868869781,"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":0.08820000290870667,"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/oracle","display_name":"Oracle","score":0.6062999963760376},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5975000262260437},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.47859999537467957},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.45190000534057617},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.36980000138282776},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.362199991941452},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.33970001339912415},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.322299987077713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8510000109672546},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.6062999963760376},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5975000262260437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5307999849319458},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.47859999537467957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46889999508857727},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.45190000534057617},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33149999380111694},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.30079999566078186},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C19012869","wikidata":"https://www.wikidata.org/wiki/Q578372","display_name":"Response time","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.26840001344680786},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2507.20028","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.20028","pdf_url":"https://arxiv.org/pdf/2507.20028","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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.2507.20028","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.20028","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.20028","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.20028","pdf_url":"https://arxiv.org/pdf/2507.20028","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Test-Time":[0,87],"Optimization":[1],"enables":[2],"models":[3],"to":[4,6,28,150],"adapt":[5],"new":[7],"data":[8,58,107],"during":[9],"inference":[10],"by":[11,41],"updating":[12],"parameters":[13],"on-the-fly.":[14],"Recent":[15],"advances":[16],"in":[17,31,55,115,198],"Vision-Language":[18],"Models":[19],"(VLMs)":[20],"have":[21],"explored":[22],"learning":[23],"prompts":[24,99],"at":[25,66],"test":[26,123],"time":[27],"improve":[29],"performance":[30],"downstream":[32],"tasks.":[33],"In":[34],"this":[35,39],"work,":[36],"we":[37,50,83],"extend":[38],"idea":[40],"addressing":[42],"a":[43,56,67,85,116,121,129,137,145,190],"more":[44],"general":[45],"and":[46,77,97,144,169,184,192,205],"practical":[47,191],"challenge:":[48],"Can":[49],"effectively":[51],"utilize":[52],"an":[53,70],"oracle":[54],"continuous":[57],"stream":[59],"where":[60],"only":[61],"one":[62],"sample":[63,124],"is":[64],"available":[65],"time,":[68],"requiring":[69],"immediate":[71],"query":[72],"decision":[73],"while":[74,180],"respecting":[75],"latency":[76,183],"memory":[78,142,185],"constraints?":[79],"To":[80],"tackle":[81],"this,":[82],"propose":[84],"novel":[86],"Active":[88],"Learning":[89],"(TTAL)":[90],"framework":[91,188],"that":[92,104],"adaptively":[93],"queries":[94],"uncertain":[95],"samples":[96],"updates":[98],"dynamically.":[100],"Unlike":[101],"prior":[102],"methods":[103,179],"assume":[105],"batched":[106],"or":[108],"multiple":[109],"gradient":[110],"updates,":[111],"our":[112],"approach":[113],"operates":[114],"real-time":[117],"streaming":[118],"scenario":[119],"with":[120],"single":[122],"per":[125],"step.":[126],"We":[127],"introduce":[128],"dynamically":[130],"adjusted":[131],"entropy":[132],"threshold":[133],"for":[134,141,195],"active":[135],"querying,":[136],"class-balanced":[138],"replacement":[139],"strategy":[140],"efficiency,":[143],"class-aware":[146],"distribution":[147],"alignment":[148],"technique":[149],"enhance":[151],"adaptation.":[152],"The":[153],"design":[154],"choices":[155],"are":[156],"justified":[157],"using":[158],"careful":[159],"theoretical":[160],"analysis.":[161],"Extensive":[162],"experiments":[163],"across":[164],"10":[165],"cross-dataset":[166],"transfer":[167],"benchmarks":[168],"4":[170],"domain":[171],"generalization":[172],"datasets":[173],"demonstrate":[174],"consistent":[175],"improvements":[176],"over":[177],"state-of-the-art":[178],"maintaining":[181],"reasonable":[182],"overhead.":[186],"Our":[187],"provides":[189],"effective":[193],"solution":[194],"real-world":[196],"deployment":[197],"safety-critical":[199],"applications":[200],"such":[201],"as":[202],"autonomous":[203],"systems":[204],"medical":[206],"diagnostics.":[207]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}