{"id":"https://openalex.org/W7161264524","doi":"https://doi.org/10.48550/arxiv.2605.14913","title":"Representative Attention For Vision Transformers","display_name":"Representative Attention For Vision Transformers","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161264524","doi":"https://doi.org/10.48550/arxiv.2605.14913"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14913","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14913","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.2605.14913","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128990801","display_name":"Yuntong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuntong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129717807","display_name":"Hainuo Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hainuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136265304","display_name":"Hengxing Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Hengxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136223788","display_name":"Mingjia Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Mingjia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136244476","display_name":"Xiaojie Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Xiaojie","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/T10036","display_name":"Advanced Neural Network Applications","score":0.19110000133514404,"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":0.19110000133514404,"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":0.18809999525547028,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.09059999883174896,"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/security-token","display_name":"Security token","score":0.7574999928474426},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.47600001096725464},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.42820000648498535},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4108999967575073},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3544999957084656},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.3418000042438507},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.3314000070095062},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.31850001215934753},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3172999918460846}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7674000263214111},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7574999928474426},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4408000111579895},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4108999967575073},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3959999978542328},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.376800000667572},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3418000042438507},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C110157686","wikidata":"https://www.wikidata.org/wiki/Q922122","display_name":"Broadcasting (networking)","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C2781142347","wikidata":"https://www.wikidata.org/wiki/Q1366592","display_name":"Hilbert curve","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2921000123023987},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2863999903202057},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.27720001339912415},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14913","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14913","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.2605.14913","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14913","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Linear":[0],"attention":[1,80],"has":[2],"emerged":[3],"as":[4],"a":[5,28,77,102,123,147],"promising":[6],"direction":[7],"for":[8],"scaling":[9,198],"Vision":[10],"Transformers":[11],"beyond":[12],"the":[13,62,153,182,189,202,229],"quadratic":[14,195],"cost":[15],"of":[16,31,65,91,105,117,185,204,231],"dense":[17],"self-attention.":[18],"A":[19],"prevalent":[20],"strategy":[21],"is":[22],"to":[23,54,57,109,114,157,196,201],"compress":[24],"spatial":[25,49,97,119,159,205],"tokens":[26,46,94,108,128,135,160],"into":[27,133],"compact":[29,103,148],"set":[30,104],"intermediate":[32,93],"proxies":[33],"that":[34,82],"mediate":[35],"global":[36,79,144,173,210],"information":[37,155],"exchange.":[38],"However,":[39],"existing":[40],"methods":[41],"typically":[42],"derive":[43],"these":[44],"proxy":[45],"from":[47,95,194],"predefined":[48],"layouts,":[50],"causing":[51],"token":[52,84,179,191],"compression":[53,85],"remain":[55],"anchored":[56],"image":[58,221],"coordinates":[59],"rather":[60],"than":[61],"semantic":[63,226],"organization":[64],"visual":[66],"content.":[67],"To":[68],"overcome":[69],"this":[70],"limitation,":[71],"we":[72],"propose":[73],"Representative":[74],"Attention":[75],"(RPAttention),":[76],"linear":[78,197],"mechanism":[81],"performs":[83],"directly":[86],"in":[87],"representation":[88],"space.":[89],"Instead":[90],"constructing":[92],"fixed":[96],"partitions,":[98],"it":[99],"dynamically":[100],"forms":[101],"learned":[106],"representative":[107,134],"enable":[110],"semantically":[111],"related":[112],"regions":[113],"communicate":[115],"regardless":[116],"their":[118],"distance,":[120],"by":[121],"following":[122],"lightweight":[124],"Gather-Interact-Distribute":[125],"paradigm.":[126],"Spatial":[127],"are":[129],"first":[130],"softly":[131],"gathered":[132],"through":[136],"competitive":[137],"similarity-based":[138],"routing.":[139],"The":[140],"representatives":[141],"then":[142],"perform":[143],"interaction":[145,192],"within":[146],"latent":[149],"space,":[150],"before":[151],"broadcasting":[152],"refined":[154],"back":[156],"all":[158],"via":[161],"query-driven":[162],"cross-attention.":[163],"Via":[164],"replacing":[165],"coordinate-driven":[166],"aggregation":[167],"with":[168,181,199],"representation-driven":[169],"compression,":[170],"RPAttention":[171],"preserves":[172],"receptive":[174],"fields":[175],"while":[176,207],"adaptively":[177],"aligning":[178],"communication":[180],"content":[183],"structure":[184],"each":[186],"input.RPAttention":[187],"reduces":[188],"dominant":[190],"complexity":[193],"respect":[200],"number":[203],"tokens,":[206],"maintaining":[208],"expressive":[209],"context":[211],"modeling.":[212],"Extensive":[213],"experiments":[214],"across":[215],"diverse":[216],"vision":[217],"transformer":[218],"backbones":[219],"on":[220],"classification,":[222],"object":[223],"detection,":[224],"and":[225],"segmentation":[227],"demonstrate":[228],"effectiveness":[230],"our":[232],"design.":[233]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}