{"id":"https://openalex.org/W7164890608","doi":"https://doi.org/10.48550/arxiv.2606.16448","title":"Hierarchical Fine-Grained Aerial Object Detection","display_name":"Hierarchical Fine-Grained Aerial Object Detection","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164890608","doi":"https://doi.org/10.48550/arxiv.2606.16448"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16448","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.16448","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138736601","display_name":"Yan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138717704","display_name":"Fang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Fang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138739944","display_name":"Wen Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Wen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138725140","display_name":"Gui-Song Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Gui-Song","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9301000237464905,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9301000237464905,"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":0.015200000256299973,"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":0.012000000104308128,"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/object-detection","display_name":"Object detection","score":0.6891999840736389},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6324999928474426},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6304000020027161},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6162999868392944},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5871000289916992},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.4941999912261963},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4763000011444092},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4519999921321869},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.4401000142097473},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.3741999864578247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7159000039100647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6988000273704529},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6891999840736389},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6324999928474426},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6304000020027161},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6162999868392944},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5871000289916992},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4519999921321869},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.4401000142097473},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43149998784065247},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.3716000020503998},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3303999900817871},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C20894473","wikidata":"https://www.wikidata.org/wiki/Q1116105","display_name":"Object model","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2955999970436096},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C13672336","wikidata":"https://www.wikidata.org/wiki/Q3460803","display_name":"Bag-of-words model","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C53073257","wikidata":"https://www.wikidata.org/wiki/Q7075021","display_name":"Object-oriented design","level":3,"score":0.27619999647140503},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.2743000090122223},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26600000262260437},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.26260000467300415},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16448","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.16448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16448","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6907978057861328}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fine-grained":[0],"aerial":[1,85,168,190],"object":[2,11,30,86,156,191,199],"detection,":[3,31],"driven":[4],"by":[5,71,103],"the":[6,26,112,181,203],"intrinsic":[7],"granularity":[8],"of":[9,28,162,185],"real-world":[10],"categories,":[12],"is":[13],"crucial":[14],"for":[15,49,159],"advanced":[16],"scene":[17],"understanding":[18],"in":[19],"remote":[20],"sensing.":[21],"Existing":[22],"methods":[23],"largely":[24],"inherit":[25],"paradigm":[27],"coarse-grained":[29],"relying":[32],"solely":[33],"on":[34,134,202],"single-label":[35],"supervision":[36],"and":[37,62,137,165,175,222],"thus":[38],"struggling":[39],"to":[40,82,114,141,194],"distinguish":[41],"model-level":[42],"categories":[43,187],"with":[44,100],"subtle":[45,116],"structural":[46,117],"differences.":[47],"However,":[48],"each":[50],"specific":[51],"model":[52],"(e.g.,":[53],"Boeing":[54],"787),":[55],"structured":[56],"prior":[57],"knowledge":[58],"such":[59],"as":[60],"attributes":[61,107],"hierarchies":[63],"offers":[64],"discriminative":[65],"semantics":[66,99],"across":[67,216],"multiple":[68],"granularities.":[69],"Motivated":[70],"this,":[72],"we":[73,89,151],"present":[74],"ExpertDet,":[75],"a":[76,129,153],"scheme":[77],"that":[78,209],"incorporates":[79],"expert-informed":[80],"cues":[81],"enhance":[83],"fine-grained":[84,155,214],"detection.":[87],"Specifically,":[88],"design":[90],"Vision-aware":[91],"Masked":[92],"Attribute":[93],"Modeling":[94],"(VMAM),":[95],"which":[96,127],"aligns":[97],"attribute":[98],"visual":[101,109,130],"structures":[102],"reconstructing":[104],"randomly":[105],"masked":[106],"from":[108,167],"cues,":[110],"enabling":[111],"detector":[113],"capture":[115],"distinctions.":[118],"We":[119,196],"further":[120],"propose":[121],"Hierarchical":[122],"Visual":[123],"Instance":[124],"Promotion":[125],"(HierVIP),":[126],"builds":[128],"prototype":[131],"tree":[132],"based":[133],"hierarchical":[135],"relations":[136],"imposes":[138],"taxonomy-aware":[139],"constraints":[140],"preserve":[142],"cross-level":[143],"semantic":[144],"continuity":[145],"while":[146],"enhancing":[147],"category":[148],"discrimination.":[149],"Moreover,":[150],"curate":[152],"new":[154],"detection":[157,192,200],"benchmark":[158,197],"Precise":[160],"recognition":[161],"model-specific":[163,186],"Ships":[164],"Planes":[166],"imagery,":[169],"PSP,":[170],"covering":[171],"106":[172],"ship":[173],"classes":[174],"30":[176],"airplane":[177],"models,":[178],"respectively,":[179],"featuring":[180],"most":[182],"extensive":[183],"collection":[184],"among":[188],"existing":[189],"datasets":[193],"date.":[195],"state-of-the-art":[198],"algorithms":[201],"PSP":[204],"benchmark.":[205],"Extensive":[206],"evaluation":[207],"demonstrates":[208],"ExpertDet":[210],"consistently":[211],"outperforms":[212],"other":[213],"competitors":[215],"hierarchy":[217],"levels.":[218],"The":[219],"dataset,":[220],"benchmark,":[221],"code":[223],"are":[224],"available":[225],"at":[226],"https://nnnnerd.github.io/PSP-Benchmark/.":[227]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}
