{"id":"https://openalex.org/W7162002710","doi":"https://doi.org/10.48550/arxiv.2605.20738","title":"STAR-IOD: Scale-decoupled Topology Alignment with Pseudo-label Refinement for Remote Sensing Incremental Object Detection","display_name":"STAR-IOD: Scale-decoupled Topology Alignment with Pseudo-label Refinement for Remote Sensing Incremental Object Detection","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7162002710","doi":"https://doi.org/10.48550/arxiv.2605.20738"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20738","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.2605.20738","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128068192","display_name":"Yaoteng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yaoteng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136662707","display_name":"Qing Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136624720","display_name":"Junyu Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Junyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136680454","display_name":"Wang, Qi, 1971-","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Qi","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/T10689","display_name":"Remote-Sensing Image Classification","score":0.42910000681877136,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.42910000681877136,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.28369998931884766,"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":0.1979999989271164,"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/object-detection","display_name":"Object detection","score":0.5958999991416931},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5670999884605408},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5317000150680542},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.515500009059906},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5121999979019165},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.40880000591278076},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.3901999890804291},{"id":"https://openalex.org/keywords/remote-sensing-application","display_name":"Remote sensing application","score":0.319599986076355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7491999864578247},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5958999991416931},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5670999884605408},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5317000150680542},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.515500009059906},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5121999979019165},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5049999952316284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48739999532699585},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3901999890804291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3790000081062317},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.319599986076355},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31459999084472656},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2538999915122986},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20738","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.2605.20738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20738","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Remote":[0,26,161],"sensing":[1,48],"imagery":[2],"typically":[3],"arrives":[4],"in":[5,46],"the":[6,41,52,70,118,149],"form":[7],"of":[8,33,54,151],"continuous":[9],"data":[10],"streams.":[11],"Traditional":[12],"detectors":[13],"often":[14],"forget":[15],"previously":[16],"learned":[17],"categories":[18],"when":[19],"learning":[20],"new":[21],"ones;":[22],"therefore,":[23],"research":[24,172],"on":[25,173,189,203],"Sensing":[27,162],"Incremental":[28,163],"Object":[29,164],"Detection":[30,165],"(RS-IOD)":[31],"is":[32],"great":[34],"significance.":[35],"However,":[36],"existing":[37],"methods":[38],"largely":[39],"overlook":[40],"intra-class":[42,108],"scale":[43,113],"variations":[44],"prevalent":[45],"remote":[47],"scenes,":[49],"which":[50,68],"undermines":[51],"effectiveness":[53],"knowledge":[55,59],"transfer":[56,98],"and":[57,106,144,147,168,186,191,206,211],"old":[58,155],"preservation.":[60],"Moreover,":[61],"RS-IOD":[62],"also":[63,158],"suffers":[64],"from":[65],"missing":[66,152],"annotations,":[67],"cause":[69],"model":[71],"to":[72,97,130,170],"misclassify":[73],"old-class":[74],"instances":[75],"as":[76],"background.":[77],"To":[78],"address":[79],"these":[80],"challenges,":[81],"we":[82,89,116],"propose":[83],"a":[84,91,123],"novel":[85,207],"framework,":[86],"STAR-IOD.":[87],"First,":[88],"introduce":[90,117],"Subspace-decoupled":[92],"Topology":[93],"Distillation":[94],"(STD)":[95],"module":[96,125],"structural":[99],"knowledge,":[100],"explicitly":[101],"aligning":[102],"inter-class":[103],"topological":[104],"relationships":[105],"mitigating":[107],"representation":[109],"discrepancies":[110],"induced":[111],"by":[112,184],"shifts.":[114],"Furthermore,":[115],"Clustering-driven":[119],"Pseudo-label":[120],"Generator":[121],"(CPG),":[122],"plug-and-play":[124],"that":[126,178],"leverages":[127],"K-Means":[128],"clustering":[129],"dynamically":[131],"identify":[132],"class-specific":[133],"thresholds,":[134],"thereby":[135],"guaranteeing":[136],"an":[137],"accurate":[138],"distinction":[139],"between":[140],"true":[141],"positive":[142],"targets":[143],"background":[145],"noise":[146],"alleviating":[148,195],"issue":[150],"annotations":[153],"for":[154],"classes.":[156,208],"We":[157],"constructed":[159],"two":[160],"datasets,":[166],"DIOR-IOD":[167,190],"DOTA-IOD":[169],"facilitate":[171],"RS-IOD.":[174],"Extensive":[175],"experiments":[176],"demonstrate":[177],"our":[179],"method":[180],"outperforms":[181],"state-of-the-art":[182],"approaches":[183],"1.7%":[185],"2.1%":[187],"mAP":[188],"DOTA-IOD,":[192],"respectively,":[193],"effectively":[194],"catastrophic":[196],"forgetting":[197],"while":[198],"preserving":[199],"strong":[200],"detection":[201],"performance":[202],"both":[204],"base":[205],"The":[209],"code":[210],"dataset":[212],"are":[213],"released":[214],"at:":[215],"https://github.com/zyt95579/STAR-IOD.":[216]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}