{"id":"https://openalex.org/W7141624553","doi":"https://doi.org/10.48550/arxiv.2603.25573","title":"Hierarchy-Guided Multimodal Representation Learning for Taxonomic Inference","display_name":"Hierarchy-Guided Multimodal Representation Learning for Taxonomic Inference","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7141624553","doi":"https://doi.org/10.48550/arxiv.2603.25573"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25573","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.2603.25573","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121735453","display_name":"Sk Miraj Ahmed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed, Sk Miraj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130774243","display_name":"Xi Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Xi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130748581","display_name":"Yunqi Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yunqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130778836","display_name":"Yuewei Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Yuewei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130721574","display_name":"Wei Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Wei","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/T10895","display_name":"Species Distribution and Climate Change","score":0.304500013589859,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10895","display_name":"Species Distribution and Climate Change","score":0.304500013589859,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12640","display_name":"Environmental DNA in Biodiversity Studies","score":0.1931000053882599,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.08380000293254852,"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/inference","display_name":"Inference","score":0.603600025177002},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5371000170707703},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49059998989105225},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4352000057697296},{"id":"https://openalex.org/keywords/biodiversity","display_name":"Biodiversity","score":0.43230000138282776},{"id":"https://openalex.org/keywords/taxonomy","display_name":"Taxonomy (biology)","score":0.42100000381469727},{"id":"https://openalex.org/keywords/biological-data","display_name":"Biological data","score":0.34689998626708984},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.3070000112056732}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6263999938964844},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.603600025177002},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5871000289916992},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5371000170707703},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5332000255584717},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49059998989105225},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4352000057697296},{"id":"https://openalex.org/C130217890","wikidata":"https://www.wikidata.org/wiki/Q47041","display_name":"Biodiversity","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.42100000381469727},{"id":"https://openalex.org/C201797286","wikidata":"https://www.wikidata.org/wiki/Q4914986","display_name":"Biological data","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31130000948905945},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C189592816","wikidata":"https://www.wikidata.org/wiki/Q427626","display_name":"Taxonomic rank","level":3,"score":0.29280000925064087},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25573","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.2603.25573","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25573","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":[{"id":"https://metadata.un.org/sdg/16","score":0.8433153629302979,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"biodiversity":[1,130,173],"identification":[2],"from":[3,35],"large-scale":[4,129],"field":[5],"data":[6],"is":[7,25,69,123,169],"a":[8,53,111],"foundational":[9],"problem":[10],"with":[11,147,166],"direct":[12],"impact":[13],"on":[14],"ecology,":[15],"conservation,":[16],"and":[17,57,75,103,106,122,153],"environmental":[18],"monitoring.":[19],"In":[20],"practice,":[21],"the":[22,62],"core":[23],"task":[24],"taxonomic":[26,99,135],"prediction":[27],"-":[28],"inferring":[29],"order,":[30],"family,":[31],"genus,":[32],"or":[33,44,119],"species":[34],"imperfect":[36],"inputs":[37],"such":[38],"as":[39,52],"specimen":[40],"images,":[41],"DNA":[42,155],"barcodes,":[43],"both.":[45],"Existing":[46],"multimodal":[47,85,145],"methods":[48],"often":[49],"treat":[50],"taxonomy":[51],"flat":[54],"label":[55],"space":[56],"therefore":[58],"fail":[59],"to":[60,94,125,143],"encode":[61],"hierarchical":[63],"structure":[64],"of":[65],"biological":[66,163],"classification,":[67],"which":[68,88,108],"critical":[70],"for":[71,83,171],"robustness":[72],"under":[73,151],"noise":[74],"missing":[76],"modalities.":[77],"We":[78],"present":[79],"two":[80],"end-to-end":[81],"variants":[82],"hierarchy-aware":[84],"learning:":[86],"CLiBD-HiR,":[87],"introduces":[89],"Hierarchical":[90],"Information":[91],"Regularization":[92],"(HiR)":[93],"shape":[95],"embedding":[96],"geometry":[97],"across":[98],"levels,":[100],"yielding":[101],"structured":[102],"noise-robust":[104],"representations;":[105],"CLiBD-HiR-Fuse,":[107],"additionally":[109],"trains":[110],"lightweight":[112],"fusion":[113],"predictor":[114],"that":[115,160],"supports":[116],"image-only,":[117],"DNA-only,":[118],"joint":[120],"inference":[121],"resilient":[124],"modality":[126],"corruption.":[127],"Across":[128],"benchmarks,":[131],"our":[132],"approach":[133],"improves":[134],"classification":[136],"accuracy":[137],"by":[138],"over":[139],"14":[140],"percent":[141],"compared":[142],"strong":[144],"baselines,":[146],"particularly":[148],"large":[149],"gains":[150],"partial":[152],"corrupted":[154],"conditions.":[156],"These":[157],"results":[158],"highlight":[159],"explicitly":[161],"encoding":[162],"hierarchy,":[164],"together":[165],"flexible":[167],"fusion,":[168],"key":[170],"practical":[172],"foundation":[174],"models.":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-28T00:00:00"}
