{"id":"https://openalex.org/W7165645491","doi":"https://doi.org/10.48550/arxiv.2606.22077","title":"Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction","display_name":"Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction","publication_year":2026,"publication_date":"2026-06-20","ids":{"openalex":"https://openalex.org/W7165645491","doi":"https://doi.org/10.48550/arxiv.2606.22077"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22077","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22077","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":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.2606.22077","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139157913","display_name":"Zixuan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139161837","display_name":"Kaijie Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Kaijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110723397","display_name":"Chun He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Chun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086277648","display_name":"Xiaoxu Cai","orcid":"https://orcid.org/0000-0002-2906-2358"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Xiaoxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013356232","display_name":"Xinhai Ye","orcid":"https://orcid.org/0000-0002-0203-0663"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Xinhai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139170684","display_name":"Haishuai Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haishuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040962519","display_name":"G\u014dngy\u00edn Y\u00e8","orcid":"https://orcid.org/0000-0003-4937-8867"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Gongyin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139208244","display_name":"Jiajun Bu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bu, Jiajun","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/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.32019999623298645,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.32019999623298645,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.0925000011920929,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.06239999830722809,"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/phylogenetic-tree","display_name":"Phylogenetic tree","score":0.6161999702453613},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4839000105857849},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45649999380111694},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.39579999446868896},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.36230000853538513},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.35830000042915344},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.3203999996185303}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6941999793052673},{"id":"https://openalex.org/C193252679","wikidata":"https://www.wikidata.org/wiki/Q242125","display_name":"Phylogenetic tree","level":3,"score":0.6161999702453613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4839000105857849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48240000009536743},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45649999380111694},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35830000042915344},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C90132467","wikidata":"https://www.wikidata.org/wiki/Q171184","display_name":"Phylogenetics","level":3,"score":0.2978000044822693},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2976999878883362},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C55919133","wikidata":"https://www.wikidata.org/wiki/Q7644297","display_name":"Supertree","level":4,"score":0.27230000495910645},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.2689000070095062},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.26600000262260437}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22077","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22077","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":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.2606.22077","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22077","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Morphological":[0],"traits":[1,96,140],"provide":[2],"important":[3],"evidence":[4],"for":[5,53,97,141],"phylogenetic":[6,55,99,143],"reconstruction":[7],"and":[8,38,74,107,114],"evolutionary":[9],"relationship":[10],"analysis.":[11],"Recent":[12],"image-based":[13],"approaches":[14],"have":[15],"introduced":[16],"deep":[17],"learning,":[18,77],"particularly":[19],"convolutional":[20],"models,":[21],"to":[22],"derive":[23,137],"morphological":[24,43,64],"features":[25],"from":[26],"specimen":[27,60],"images,":[28],"but":[29],"these":[30],"methods":[31],"generally":[32],"rely":[33],"on":[34],"single-modality":[35],"visual":[36,112,139],"representations":[37],"do":[39],"not":[40],"explicitly":[41],"incorporate":[42],"semantics.":[44],"This":[45],"study":[46],"proposes":[47],"a":[48,68,83],"morphology-aware":[49,138],"multimodal":[50,120],"alignment":[51,81,121],"framework":[52,58,135],"insect":[54],"reconstruction.":[56,100,144],"The":[57,87,129],"combines":[59],"images":[61],"with":[62,125],"curated":[63],"descriptions":[65],"by":[66,79],"adapting":[67],"vision":[69],"transformer":[70],"through":[71],"parameter-efficient":[72],"fine-tuning":[73],"supervised":[75],"contrastive":[76],"followed":[78],"image-text":[80],"in":[82],"shared":[84],"latent":[85],"space.":[86],"learned":[88],"image":[89],"embeddings":[90],"are":[91],"then":[92],"used":[93],"as":[94],"continuous":[95],"Bayesian":[98],"On":[101],"the":[102,126,133],"public":[103],"Rove-Tree-11":[104],"dataset,":[105],"comparative":[106],"ablation":[108],"experiments":[109],"across":[110],"multiple":[111],"backbones":[113],"feature":[115],"adaptation":[116],"strategies":[117],"demonstrate":[118],"that":[119,132],"improves":[122],"topological":[123],"agreement":[124],"reference":[127],"phylogeny.":[128],"results":[130],"indicate":[131],"proposed":[134],"can":[136],"computational":[142]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}
