{"id":"https://openalex.org/W7162419454","doi":"https://doi.org/10.48550/arxiv.2605.25799","title":"Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning","display_name":"Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162419454","doi":"https://doi.org/10.48550/arxiv.2605.25799"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.25799","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25799","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.25799","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101696728","display_name":"Shuai Yi","orcid":"https://orcid.org/0000-0002-1253-6633"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi, Shuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137021192","display_name":"Yixiong Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Yixiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137007560","display_name":"Yuhua Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137012320","display_name":"Ruixuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ruixuan","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":0.9811999797821045,"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":0.9811999797821045,"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":0.005400000140070915,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0032999999821186066,"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/discriminative-model","display_name":"Discriminative model","score":0.5156000256538391},{"id":"https://openalex.org/keywords/sink","display_name":"Sink (geography)","score":0.4837000072002411},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.47269999980926514},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.40049999952316284},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.32739999890327454}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7189000248908997},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5156000256538391},{"id":"https://openalex.org/C143050476","wikidata":"https://www.wikidata.org/wiki/Q194502","display_name":"Sink (geography)","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.47269999980926514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4260999858379364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.415800005197525},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.40049999952316284},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.25799","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25799","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.25799","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25799","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":"Preprint"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7279982566833496,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-language":[0],"models":[1],"(VLMs)":[2],"like":[3],"CLIP":[4],"have":[5],"shown":[6],"impressive":[7],"generalization":[8],"capabilities,":[9],"yet":[10],"their":[11,176],"potential":[12],"for":[13,45,102],"Cross-Domain":[14],"Few-Shot":[15],"Learning":[16],"(CDFSL)":[17],"remains":[18],"underexplored,":[19],"where":[20],"the":[21,36,77,98,107,112,117,144,149,182,188,197,215],"model":[22,118],"needs":[23],"to":[24,28,82,105,123,130,136,140,170,175],"transfer":[25],"source-domain":[26],"information":[27],"target":[29,115],"domains":[30],"with":[31,178],"scarce":[32],"training":[33],"data.":[34],"While":[35],"attention":[37,78,145],"sink":[38,79,146,203],"phenomenon":[39],"has":[40,53],"been":[41,55],"observed":[42],"in":[43,50,73],"VLMs":[44],"certain":[46],"tasks,":[47],"its":[48],"role":[49],"CDFSL":[51,74],"scenarios":[52],"not":[54],"studied.":[56],"In":[57],"this":[58,89],"paper,":[59],"we":[60,94,165],"uncover":[61],"a":[62,120,167],"critical":[63],"issue":[64],"overlooked":[65],"by":[66],"prior":[67],"works:":[68],"standard":[69],"target-domain":[70,131,179,183],"few-shot":[71],"fine-tuning":[72],"significantly":[75],"exacerbates":[76],"problem,":[80],"leading":[81],"poor":[83],"discriminability":[84],"across":[85],"classes.":[86],"To":[87,162],"understand":[88],"phenomenon,":[90],"through":[91],"extensive":[92],"experiments,":[93],"interpret":[95],"it":[96],"as":[97],"model's":[99,189],"shortcut":[100],"learning":[101,152,198],"domain":[103,109],"adaptation:":[104],"overcome":[106],"huge":[108],"gap":[110],"between":[111],"source":[113],"and":[114,147,195,205],"domains,":[116],"shows":[119],"high":[121],"tendency":[122],"push":[124],"tokens":[125,158,173,194,204],"that":[126],"are":[127,226],"initially":[128,156],"closer":[129,139],"classes":[132,180],"(i.e.,":[133,159],"simple":[134,193],"tokens)":[135],"be":[137],"even":[138],"these":[141,192],"classes,":[142],"exacerbating":[143],"wasting":[148],"capability":[150],"of":[151,199,217],"other":[153],"discriminative":[154],"but":[155],"further":[157],"hard":[160,200],"tokens).":[161],"address":[163],"this,":[164],"propose":[166],"novel":[168],"approach":[169],"dynamically":[171],"re-weight":[172],"according":[174],"relevance":[177],"during":[181],"finetuning,":[184],"which":[185],"explicitly":[186],"suppresses":[187],"reliance":[190],"on":[191,210],"enhances":[196],"tokens,":[201],"reducing":[202],"enhancing":[206],"discriminability.":[207],"Extensive":[208],"experiments":[209],"four":[211],"benchmark":[212],"datasets":[213],"validate":[214],"rationale":[216],"our":[218],"method,":[219],"demonstrating":[220],"new":[221],"state-of-the-art":[222],"performance.":[223],"Our":[224],"codes":[225],"available":[227],"at":[228],"https://github.com/shuaiyi308/TIR.":[229]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}