{"id":"https://openalex.org/W7125509760","doi":"https://doi.org/10.48550/arxiv.2601.15688","title":"Performance-guided Reinforced Active Learning for Object Detection","display_name":"Performance-guided Reinforced Active Learning for Object Detection","publication_year":2026,"publication_date":"2026-01-22","ids":{"openalex":"https://openalex.org/W7125509760","doi":"https://doi.org/10.48550/arxiv.2601.15688"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.15688","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.15688","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":"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.2601.15688","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102953752","display_name":"Zhixuan Liang","orcid":"https://orcid.org/0000-0002-4851-1278"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Zhixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060379760","display_name":"Xingyu Zeng","orcid":"https://orcid.org/0009-0007-8224-4461"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Xingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123642859","display_name":"Rui Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123643455","display_name":"Ping Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Ping","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/T12072","display_name":"Machine Learning and Algorithms","score":0.8791000247001648,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.8791000247001648,"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":0.02449999935925007,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.022199999541044235,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7164999842643738},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.703000009059906},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6554999947547913},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.5746999979019165},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.45660001039505005},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.44699999690055847},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4408000111579895},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.38909998536109924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7203999757766724},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7164999842643738},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.703000009059906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6937999725341797},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6554999947547913},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.5746999979019165},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5511999726295471},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.45660001039505005},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4408000111579895},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3684999942779541},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3449999988079071},{"id":"https://openalex.org/C2779542340","wikidata":"https://www.wikidata.org/wiki/Q1062461","display_name":"Learning object","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C199190896","wikidata":"https://www.wikidata.org/wiki/Q3509276","display_name":"Learning classifier system","level":3,"score":0.33469998836517334},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33250001072883606},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2703999876976013}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.15688","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.15688","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":"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.2601.15688","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.15688","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":"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Active":[0,62],"learning":[1,149],"(AL)":[2],"strategies":[3],"aim":[4],"to":[5,23,122],"train":[6],"high-performance":[7],"models":[8],"with":[9,42,116,130,138,167],"minimal":[10],"labeling":[11],"efforts,":[12],"only":[13],"selecting":[14],"the":[15,30,73,84,93,124,163],"most":[16],"informative":[17],"instances":[18],"for":[19,64],"annotation.":[20],"Current":[21],"approaches":[22],"evaluating":[24],"data":[25],"informativeness":[26],"predominantly":[27],"focus":[28],"on":[29,151],"data's":[31],"distribution":[32],"or":[33],"intrinsic":[34],"information":[35],"content":[36],"and":[37,92,99,157],"do":[38],"not":[39],"directly":[40],"correlate":[41],"downstream":[43],"task":[44],"performance,":[45],"such":[46],"as":[47,80,119],"mean":[48],"average":[49],"precision":[50],"(mAP)":[51],"in":[52,174],"object":[53,178],"detection.":[54,179],"Thus,":[55],"we":[56],"propose":[57],"Performance-guided":[58],"(i.e.":[59],"mAP-guided)":[60],"Reinforced":[61],"Learning":[63],"Object":[65],"Detection":[66],"(MGRAL),":[67],"a":[68,105,171],"novel":[69],"approach":[70,161],"that":[71,110],"leverages":[72],"concept":[74],"of":[75,88,127],"expected":[76],"model":[77,97],"output":[78],"changes":[79],"informativeness.":[81],"To":[82],"address":[83],"combinatorial":[85],"explosion":[86],"challenge":[87],"batch":[89],"sample":[90],"selection":[91,112],"non-differentiable":[94],"correlation":[95],"between":[96],"performance":[98,150],"selected":[100],"batches,":[101],"MGRAL":[102,133],"skillfully":[103],"employs":[104],"reinforcement":[106,175],"learning-based":[107],"sampling":[108],"agent":[109],"optimizes":[111],"using":[113],"policy":[114],"gradient":[115],"mAP":[117,128],"improvement":[118],"reward.":[120],"Moreover,":[121],"reduce":[123],"computational":[125],"overhead":[126],"estimation":[129],"unlabeled":[131],"samples,":[132],"utilizes":[134],"an":[135],"unsupervised":[136],"way":[137],"fast":[139],"look-up":[140],"tables,":[141],"ensuring":[142],"feasible":[143],"deployment.":[144],"We":[145],"evaluate":[146],"MGRAL's":[147],"active":[148,177],"detection":[152],"tasks":[153],"over":[154],"PASCAL":[155],"VOC":[156],"COCO":[158],"benchmarks.":[159],"Our":[160],"demonstrates":[162],"highest":[164],"AL":[165],"curve":[166],"convincing":[168],"visualizations,":[169],"establishing":[170],"new":[172],"paradigm":[173],"learning-driven":[176]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-01-24T00:00:00"}