{"id":"https://openalex.org/W7148446553","doi":"https://doi.org/10.48550/arxiv.2604.00086","title":"Hierarchical Pre-Training of Vision Encoders with Large Language Models","display_name":"Hierarchical Pre-Training of Vision Encoders with Large Language Models","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7148446553","doi":"https://doi.org/10.48550/arxiv.2604.00086"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00086","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00086","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.00086","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101765015","display_name":"Eugene Lee","orcid":"https://orcid.org/0000-0001-9967-1186"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lee, Eugene","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132783434","display_name":"Ting-Yu Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Ting-Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132789751","display_name":"Jui-Huang Tsai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tsai, Jui-Huang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132824668","display_name":"Jiajie Diao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diao, Jiajie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132764476","display_name":"Chen-Yi Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Chen-Yi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101765015"],"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":0.22419999539852142,"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":0.22419999539852142,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.11559999734163284,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.1005999967455864,"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/encoder","display_name":"Encoder","score":0.7311999797821045},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6187999844551086},{"id":"https://openalex.org/keywords/machine-vision","display_name":"Machine vision","score":0.5009999871253967},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.46209999918937683},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44510000944137573},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4415000081062317},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3725000023841858},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.3239000141620636}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8015000224113464},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.7311999797821045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6495000123977661},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6187999844551086},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.5009999871253967},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49160000681877136},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.46209999918937683},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44510000944137573},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3725000023841858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3400000035762787},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.3239000141620636},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3012999892234802},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00086","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00086","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.00086","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00086","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4560869336128235,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"field":[1],"of":[2,35,47,150],"computer":[3],"vision":[4,11,22,63,102],"has":[5],"experienced":[6],"significant":[7],"advancements":[8],"through":[9],"scalable":[10],"encoders":[12,23],"and":[13,24,65,85,110,143,160],"multimodal":[14,112],"pre-training":[15],"frameworks.":[16],"However,":[17],"existing":[18],"approaches":[19],"often":[20],"treat":[21],"large":[25],"language":[26],"models":[27],"(LLMs)":[28],"as":[29,139],"independent":[30],"modules,":[31],"limiting":[32],"the":[33,62,101,105,148,155],"integration":[34],"hierarchical":[36,59,151],"visual":[37],"features.":[38],"In":[39],"this":[40,90],"work,":[41],"we":[42,92],"propose":[43],"HIVE":[44,74,118],"(Hierarchical":[45],"Pre-Training":[46],"Vision":[48],"Encoders),":[49],"a":[50,94],"novel":[51],"framework":[52],"that":[53,70,98,117],"enhances":[54],"vision-language":[55,131,162],"alignment":[56],"by":[57],"introducing":[58],"cross-attention":[60],"between":[61],"encoder":[64,103],"LLM.":[66],"Unlike":[67],"conventional":[68],"methods":[69,135],"flatten":[71],"image":[72,125],"embeddings,":[73],"enables":[75],"structured":[76],"feature":[77,152],"fusion":[78],"across":[79],"multiple":[80],"layers,":[81],"improving":[82],"gradient":[83],"flow":[84],"representation":[86],"learning.":[87],"To":[88],"optimize":[89],"interaction,":[91],"introduce":[93],"three-stage":[95],"training":[96],"strategy":[97],"progressively":[99],"aligns":[100],"with":[104],"LLM,":[106],"ensuring":[107],"stable":[108],"optimization":[109],"effective":[111],"fusion.":[113],"Empirical":[114],"evaluations":[115],"demonstrate":[116],"achieves":[119],"superior":[120],"performance":[121],"not":[122],"only":[123],"in":[124,136],"classification":[126],"but":[127],"also":[128],"on":[129],"various":[130],"tasks,":[132],"outperforming":[133],"self-attention-based":[134],"benchmarks":[137],"such":[138],"MME,":[140],"GQA,":[141],"OK-VQA,":[142],"ScienceQA.":[144],"Our":[145],"results":[146],"highlight":[147],"benefits":[149],"integration,":[153],"paving":[154],"way":[156],"for":[157],"more":[158],"efficient":[159],"expressive":[161],"models.":[163]},"counts_by_year":[],"updated_date":"2026-04-03T16:44:17.987007","created_date":"2026-04-03T00:00:00"}