{"id":"https://openalex.org/W7138847164","doi":"https://doi.org/10.48550/arxiv.2603.17655","title":"Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment","display_name":"Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment","publication_year":2026,"publication_date":"2026-03-18","ids":{"openalex":"https://openalex.org/W7138847164","doi":"https://doi.org/10.48550/arxiv.2603.17655"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.17655","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17655","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.2603.17655","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129761948","display_name":"Yaze Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhao, Yaze","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129911830","display_name":"Yixiong Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Yixiong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130029333","display_name":"Yuhua Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuhua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129926998","display_name":"Ruixuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ruixuan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5129761948"],"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":0.9017000198364258,"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":0.9017000198364258,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06759999692440033,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.005100000184029341,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8461999893188477},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.7318999767303467},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5365999937057495},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5004000067710876},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.43650001287460327},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.3912999927997589},{"id":"https://openalex.org/keywords/sensory-cue","display_name":"Sensory cue","score":0.37929999828338623},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.37860000133514404}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8461999893188477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7530999779701233},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.7318999767303467},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7042999863624573},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5365999937057495},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5004000067710876},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.43650001287460327},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.3912999927997589},{"id":"https://openalex.org/C111370547","wikidata":"https://www.wikidata.org/wiki/Q7451120","display_name":"Sensory cue","level":2,"score":0.37929999828338623},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.37860000133514404},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35019999742507935},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3499999940395355},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3303000032901764},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C2779757391","wikidata":"https://www.wikidata.org/wiki/Q6002292","display_name":"Image translation","level":3,"score":0.29429998993873596},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25850000977516174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.17655","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17655","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.2603.17655","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17655","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Cross-Domain":[0],"Few-Shot":[1],"Learning":[2],"(CDFSL)":[3],"adapts":[4],"models":[5,27,57],"trained":[6],"with":[7,17,154],"large-scale":[8],"general":[9],"data":[10,99],"(source":[11],"domain)":[12],"to":[13,126,141,182,211,226],"downstream":[14,37],"target":[15],"domains":[16],"only":[18],"scarce":[19,97],"training":[20,98],"data,":[21],"where":[22],"the":[23,33,93,114,127,146,151,178,183,189,196,217,223,247,253],"research":[24],"on":[25,61,69,234],"vision-language":[26,249],"(e.g.,":[28],"CLIP)":[29],"is":[30],"still":[31],"in":[32,72,82,87,118,131,195],"early":[34],"stages.":[35],"Typical":[36],"domains,":[38],"such":[39,102],"as":[40],"medical":[41],"diagnosis,":[42],"require":[43],"fine-grained":[44],"visual":[45,134,160,171,197,209],"cues":[46],"for":[47,216],"interpretable":[48],"recognition,":[49],"but":[50],"we":[51,90,112,139,149,199,242],"find":[52,91],"that":[53,92,107],"current":[54,76],"fine-tuned":[55],"CLIP":[56],"can":[58,66,243],"hardly":[59],"focus":[60,68],"these":[62],"cues,":[63],"albeit":[64],"they":[65],"roughly":[67],"important":[70],"regions":[71],"source":[73],"domains.":[74],"Although":[75],"works":[77],"have":[78],"demonstrated":[79],"CLIP's":[80],"shortcomings":[81],"capturing":[83],"local":[84,115,133,159,248],"subtle":[85],"patterns,":[86,110],"this":[88,123],"paper,":[89],"domain":[94],"gap":[95],"and":[96,136,165,176,220,238,258,264],"further":[100,200],"exacerbate":[101],"shortcomings,":[103],"much":[104],"more":[105],"than":[106],"of":[108,129,255],"holistic":[109],"which":[111,157,206],"call":[113],"misalignment":[116],"problem":[117],"CLIP-based":[119],"CDFSL.":[120],"To":[121,187],"address":[122],"problem,":[124],"due":[125],"lack":[128],"supervision":[130],"aligning":[132],"features":[135,161,164,172,180,210,225],"text":[137,163],"semantics,":[138],"turn":[140],"self-supervision":[142],"information.":[143],"Inspired":[144],"by":[145,192,261],"translation":[147],"task,":[148],"propose":[150,201],"CC-CDFSL":[152],"method":[153],"cycle":[155],"consistency,":[156],"translates":[158,167],"into":[162,170],"then":[166,221],"them":[168],"back":[169,185],"(and":[173],"vice":[174],"versa),":[175],"constrains":[177],"original":[179],"close":[181],"translated":[184],"features.":[186],"reduce":[188],"noise":[190],"imported":[191],"richer":[193],"information":[194],"modality,":[198],"a":[202,213],"Semantic":[203],"Anchor":[204],"mechanism,":[205],"first":[207],"augments":[208],"provide":[212],"larger":[214],"corpus":[215],"text-to-image":[218],"mapping,":[219],"shrinks":[222],"image":[224],"filter":[227],"out":[228],"irrelevant":[229],"image-to-text":[230],"mapping.":[231],"Extensive":[232],"experiments":[233],"various":[235],"benchmarks,":[236],"backbones,":[237],"fine-tuning":[239],"methods":[240],"show":[241],"(1)":[244],"effectively":[245],"improve":[246],"alignment,":[250],"(2)":[251],"enhance":[252],"interpretability":[254],"learned":[256],"patterns":[257],"model":[259],"decisions":[260],"visualizing":[262],"patches,":[263],"(3)":[265],"achieve":[266],"state-of-the-art":[267],"performance.":[268]},"counts_by_year":[],"updated_date":"2026-03-20T20:54:20.808490","created_date":"2026-03-20T00:00:00"}
