{"id":"https://openalex.org/W7155055529","doi":"https://doi.org/10.48550/arxiv.2604.18376","title":"Towards Robust Text-to-Image Person Retrieval: Multi-View Reformulation for Semantic Compensation","display_name":"Towards Robust Text-to-Image Person Retrieval: Multi-View Reformulation for Semantic Compensation","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155055529","doi":"https://doi.org/10.48550/arxiv.2604.18376"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.18376","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18376","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.18376","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134188237","display_name":"Chao Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yuan, Chao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134128572","display_name":"Yujian Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Yujian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134206085","display_name":"Haoxuan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Haoxuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134197891","display_name":"Guanglin Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Guanglin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5134188237"],"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.5378999710083008,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.5378999710083008,"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":0.19850000739097595,"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/T11448","display_name":"Face recognition and analysis","score":0.07540000230073929,"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/robustness","display_name":"Robustness (evolution)","score":0.6686000227928162},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5605999827384949},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.5379999876022339},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5152999758720398},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.44920000433921814},{"id":"https://openalex.org/keywords/feature-model","display_name":"Feature model","score":0.40869998931884766},{"id":"https://openalex.org/keywords/semantic-equivalence","display_name":"Semantic equivalence","score":0.38670000433921814},{"id":"https://openalex.org/keywords/rewriting","display_name":"Rewriting","score":0.3594000041484833},{"id":"https://openalex.org/keywords/semantic-compression","display_name":"Semantic compression","score":0.353300005197525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7440000176429749},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6686000227928162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6201000213623047},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5605999827384949},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.5379999876022339},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5152999758720398},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.44920000433921814},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4465000033378601},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.40869998931884766},{"id":"https://openalex.org/C37926939","wikidata":"https://www.wikidata.org/wiki/Q7449061","display_name":"Semantic equivalence","level":4,"score":0.38670000433921814},{"id":"https://openalex.org/C154690210","wikidata":"https://www.wikidata.org/wiki/Q1668499","display_name":"Rewriting","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C202708506","wikidata":"https://www.wikidata.org/wiki/Q7449050","display_name":"Semantic compression","level":5,"score":0.353300005197525},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3077999949455261},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.30469998717308044},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C522192633","wikidata":"https://www.wikidata.org/wiki/Q34228","display_name":"Sign language","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.26339998841285706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.18376","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18376","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.18376","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18376","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":"article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5291743874549866}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"text-to-image":[1,176],"person":[2,177],"retrieval":[3,178],"tasks,":[4],"the":[5,12,20,34,43,162,165],"diversity":[6],"of":[7,14,22,45,164],"natural":[8],"language":[9],"expressions":[10],"and":[11,71,98,127,171],"implicitness":[13],"visual":[15,152],"semantics":[16],"often":[17],"lead":[18],"to":[19,38,101,150],"problem":[21],"Expression":[23],"Drift,":[24],"where":[25],"semantically":[26,103],"equivalent":[27,104],"texts":[28],"exhibit":[29],"significant":[30],"feature":[31,72,89,96,125],"discrepancies":[32],"in":[33],"embedding":[35],"space":[36,117],"due":[37],"phrasing":[39],"variations,":[40],"thereby":[41],"degrading":[42],"robustness":[44],"image-text":[46],"alignment.":[47],"This":[48],"paper":[49],"proposes":[50],"a":[51],"semantic":[52,69,153],"compensation":[53,118],"framework":[54],"(MVR)":[55],"driven":[56],"by":[57],"Large":[58],"Language":[59],"Models":[60],"(LLMs),":[61],"which":[62,142],"enhances":[63],"cross-modal":[64],"representation":[65],"consistency":[66],"through":[67,123,146],"multi-view":[68,124],"reformulation":[70,149],"compensation.":[73],"The":[74],"core":[75],"methodology":[76],"comprises":[77],"three":[78,175],"components:":[79],"Multi-View":[80],"Reformulation":[81],"(MVR):":[82],"A":[83,114],"dual-branch":[84],"prompting":[85],"strategy":[86],"combines":[87],"key":[88],"guidance":[90],"(extracting":[91],"visually":[92],"critical":[93],"components":[94],"via":[95],"similarity)":[97],"diversity-aware":[99],"rewriting":[100],"generate":[102],"yet":[105],"distributionally":[106],"diverse":[107],"textual":[108],"variants;":[109],"Textual":[110],"Feature":[111],"Robustness":[112],"Enhancement:":[113],"training-free":[115],"latent":[116],"mechanism":[119],"suppresses":[120],"noise":[121],"interference":[122],"mean-pooling":[126],"residual":[128],"connections,":[129],"effectively":[130],"capturing":[131],"\"Semantic":[132],"Echoes\";":[133],"Visual":[134],"Semantic":[135],"Compensation:":[136],"VLM":[137],"generates":[138],"multi-perspective":[139],"image":[140],"descriptions,":[141],"are":[143],"further":[144],"enhanced":[145],"shared":[147],"text":[148],"address":[151],"gaps.":[154],"Experiments":[155],"demonstrate":[156],"that":[157],"our":[158],"method":[159],"can":[160],"improve":[161],"accuracy":[163],"original":[166],"model":[167],"well":[168],"without":[169],"training":[170],"performs":[172],"SOTA":[173],"on":[174],"datasets.":[179]},"counts_by_year":[],"updated_date":"2026-04-22T06:07:44.442478","created_date":"2026-04-22T00:00:00"}
