{"id":"https://openalex.org/W7155405363","doi":"https://doi.org/10.48550/arxiv.2604.20191","title":"From Scene to Object: Text-Guided Dual-Gaze Prediction","display_name":"From Scene to Object: Text-Guided Dual-Gaze Prediction","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155405363","doi":"https://doi.org/10.48550/arxiv.2604.20191"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20191","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.2604.20191","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134439990","display_name":"Zehong Ke","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ke, Zehong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128031622","display_name":"Yanbo Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Yanbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134372591","display_name":"Jinhao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jinhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134448436","display_name":"Zhiyuan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091181503","display_name":"Yiqian Tu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tu, Yiqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129463357","display_name":"Qingwen Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Qingwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042865025","display_name":"Heye Huang","orcid":"https://orcid.org/0000-0002-8964-8764"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Heye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134404109","display_name":"Jianqiang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jianqiang","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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.5295000076293945,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.5295000076293945,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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":0.19429999589920044,"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":0.1534000039100647,"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/bottleneck","display_name":"Bottleneck","score":0.5698000192642212},{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.5277000069618225},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.5005000233650208},{"id":"https://openalex.org/keywords/cognitive-architecture","display_name":"Cognitive architecture","score":0.4311000108718872},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.36739999055862427},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.36390000581741333},{"id":"https://openalex.org/keywords/information-bottleneck-method","display_name":"Information bottleneck method","score":0.3515999913215637},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3431999981403351},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.34310001134872437}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7713000178337097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.61080002784729},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5698000192642212},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.5277000069618225},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.5005000233650208},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46869999170303345},{"id":"https://openalex.org/C20854674","wikidata":"https://www.wikidata.org/wiki/Q4386060","display_name":"Cognitive architecture","level":3,"score":0.4311000108718872},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.3515999913215637},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C577917","wikidata":"https://www.wikidata.org/wiki/Q189223","display_name":"Turing test","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C161407221","wikidata":"https://www.wikidata.org/wiki/Q4382939","display_name":"Cognitive model","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.26910001039505005},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C108154423","wikidata":"https://www.wikidata.org/wiki/Q1469792","display_name":"Salience (neuroscience)","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.25040000677108765},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.2500999867916107},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20191","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.2604.20191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20191","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Interpretable":[0],"driver":[1,88],"attention":[2,61,89,193],"prediction":[3,70],"is":[4],"crucial":[5],"for":[6,38],"human-like":[7],"autonomous":[8],"driving.":[9],"However,":[10],"existing":[11,164],"datasets":[12],"provide":[13],"only":[14],"scene-level":[15],"global":[16],"gaze":[17,69],"rather":[18],"than":[19],"fine-grained":[20],"object-level":[21,60,87,110],"annotations,":[22],"inherently":[23],"failing":[24],"to":[25,46,79,175,210],"support":[26],"text-grounded":[27],"cognitive":[28,213],"modeling.":[29],"Consequently,":[30],"while":[31],"Vision-Language":[32],"Models":[33],"(VLMs)":[34],"hold":[35],"great":[36],"potential":[37],"semantic":[39,137],"reasoning,":[40],"this":[41,55,63,121],"critical":[42],"data":[43,77,123],"limitations":[44],"leads":[45],"severe":[47],"text-vision":[48],"decoupling":[49],"and":[50,57,139],"visual-bias":[51],"hallucinations.":[52,118],"To":[53],"break":[54],"bottleneck":[56],"achieve":[58],"precise":[59,150],"prediction,":[62],"paper":[64],"proposes":[65],"a":[66,73,86,93,145,176,186],"novel":[67],"dual-branch":[68],"framework,":[71],"establishing":[72],"complete":[74],"paradigm":[75],"from":[76],"construction":[78],"model":[80,97],"architecture.":[81,129],"First,":[82],"we":[83,105,125],"construct":[84],"G-W3DA,":[85],"dataset.":[90],"By":[91],"integrating":[92],"multimodal":[94],"large":[95],"language":[96],"with":[98],"the":[99,127,133,156,192],"Segment":[100],"Anything":[101],"Model":[102],"3":[103],"(SAM3),":[104],"decouple":[106],"macroscopic":[107],"heatmaps":[108,194],"into":[109],"masks":[111],"under":[112,182],"rigorous":[113],"cross-validation,":[114],"fundamentally":[115],"eliminating":[116],"annotation":[117],"Building":[119],"upon":[120],"high-quality":[122],"foundation,":[124],"propose":[126],"DualGaze-VLM":[128,161,197],"This":[130],"architecture":[131],"extracts":[132],"hidden":[134],"states":[135],"of":[136,204],"queries":[138],"dynamically":[140],"modulates":[141],"visual":[142,187],"features":[143],"via":[144],"Condition-Aware":[146],"SE-Gate,":[147],"achieving":[148,173],"intent-driven":[149],"spatial":[151,169],"anchoring.":[152],"Extensive":[153],"experiments":[154],"on":[155],"W3DA":[157],"benchmark":[158],"demonstrate":[159],"that":[160,191],"consistently":[162],"surpasses":[163],"state-of-the-art":[165],"(SOTA)":[166],"models":[167],"in":[168,179],"alignment":[170],"metrics,":[171],"notably":[172],"up":[174],"17.8%":[177],"improvement":[178],"Similarity":[180],"(SIM)":[181],"safety-critical":[183],"scenarios.":[184],"Furthermore,":[185],"Turing":[188],"test":[189],"reveals":[190],"generated":[195],"by":[196,202],"are":[198],"perceived":[199],"as":[200],"authentic":[201],"88.22%":[203],"human":[205],"evaluators,":[206],"proving":[207],"its":[208],"capability":[209],"generate":[211],"rational":[212],"priors.":[214]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-24T00:00:00"}