{"id":"https://openalex.org/W7151524811","doi":"https://doi.org/10.48550/arxiv.2604.03841","title":"Training a Student Expert via Semi-Supervised Foundation Model Distillation","display_name":"Training a Student Expert via Semi-Supervised Foundation Model Distillation","publication_year":2026,"publication_date":"2026-04-04","ids":{"openalex":"https://openalex.org/W7151524811","doi":"https://doi.org/10.48550/arxiv.2604.03841"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03841","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03841","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.2604.03841","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5094196412","display_name":"Pardis Taghavi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taghavi, Pardis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133089143","display_name":"Tian Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133076615","display_name":"Renjie Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Renjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017696249","display_name":"Reza Langari","orcid":"https://orcid.org/0000-0001-7900-5186"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Langari, Reza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133112733","display_name":"Zhengzhong Tu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tu, Zhengzhong","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5425000190734863,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5425000190734863,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.288100004196167,"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":0.04690000042319298,"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/domain-adaptation","display_name":"Domain adaptation","score":0.718999981880188},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6814000010490417},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5031999945640564},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.4634999930858612},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4535999894142151},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44020000100135803},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4221999943256378},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.4074999988079071}],"concepts":[{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.718999981880188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7093999981880188},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6814000010490417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.661899983882904},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5609999895095825},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5031999945640564},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4074999988079071},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3790000081062317},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3783000111579895},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2720000147819519}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03841","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03841","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.2604.03841","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03841","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":{"Foundation":[0],"models":[1,33],"deliver":[2],"strong":[3],"perception":[4],"but":[5],"are":[6,54],"often":[7],"too":[8],"computationally":[9],"heavy":[10],"to":[11,86,92,107],"deploy,":[12],"and":[13,41,45,82,104,111,124,129,132,140,155,163,166],"adapting":[14],"them":[15],"typically":[16],"requires":[17],"costly":[18],"annotations.":[19],"We":[20],"introduce":[21],"a":[22,78],"semi-supervised":[23],"knowledge":[24,75],"distillation":[25],"(SSKD)":[26],"framework":[27,58],"that":[28,101],"compresses":[29],"pre-trained":[30],"vision":[31],"foundation":[32],"(VFMs)":[34],"into":[35],"compact":[36],"experts":[37],"using":[38],"limited":[39],"labeled":[40],"abundant":[42],"unlabeled":[43,136],"data,":[44],"instantiate":[46],"it":[47],"for":[48],"instance":[49],"segmentation":[50],"where":[51],"per-pixel":[52],"labels":[53],"particularly":[55],"expensive.":[56],"The":[57],"unfolds":[59],"in":[60],"three":[61],"stages:":[62],"(1)":[63],"domain":[64],"adaptation":[65,123],"of":[66],"the":[67],"VFM(s)":[68],"via":[69],"self-training":[70],"with":[71],"contrastive":[72,99,119],"calibration,":[73],"(2)":[74],"transfer":[76],"through":[77],"unified":[79],"multi-objective":[80],"loss,":[81],"(3)":[83],"student":[84,130,146],"refinement":[85],"mitigate":[87],"residual":[88],"pseudo-label":[89],"bias.":[90],"Central":[91],"our":[93,142],"approach":[94],"is":[95],"an":[96],"instance-aware":[97],"pixel-wise":[98],"loss":[100],"fuses":[102],"mask":[103],"class":[105],"scores":[106],"extract":[108],"informative":[109],"negatives":[110],"enforce":[112],"clear":[113],"inter-instance":[114],"margins.":[115],"By":[116],"maintaining":[117],"this":[118],"signal":[120],"across":[121],"both":[122],"distillation,":[125],"we":[126],"align":[127],"teacher":[128],"embeddings":[131],"more":[133],"effectively":[134],"leverage":[135],"images.":[137],"On":[138],"Cityscapes":[139],"ADE20K,":[141],"$\\approx":[143],"11\\times$":[144],"smaller":[145],"improves":[147],"over":[148],"its":[149],"zero-shot":[150],"VFM":[151],"teacher(s)":[152,160],"by":[153,161],"+11.9":[154],"+8.6":[156],"AP,":[157,165],"surpasses":[158],"adapted":[159],"+3.4":[162],"+1.5":[164],"outperforms":[167],"state-of-the-art":[168],"SSKD":[169],"methods":[170],"on":[171],"benchmarks.":[172]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}
