{"id":"https://openalex.org/W4416385168","doi":"https://doi.org/10.48550/arxiv.2510.08442","title":"Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning","display_name":"Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning","publication_year":2025,"publication_date":"2025-10-09","ids":{"openalex":"https://openalex.org/W4416385168","doi":"https://doi.org/10.48550/arxiv.2510.08442"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.08442","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08442","pdf_url":"https://arxiv.org/pdf/2510.08442","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.08442","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032545949","display_name":"Andrew Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lee, Andrew","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054161238","display_name":"Ian Chuang","orcid":"https://orcid.org/0000-0002-1983-9848"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chuang, Ian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018828704","display_name":"Dechen Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Dechen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120459856","display_name":"Kai Fukazawa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fukazawa, Kai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5045812582","display_name":"Iman Soltani","orcid":"https://orcid.org/0000-0001-9430-1522"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soltani, Iman","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5032545949"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.27219998836517334,"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":0.27219998836517334,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.2485000044107437,"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":0.13580000400543213,"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/gaze","display_name":"Gaze","score":0.6747999787330627},{"id":"https://openalex.org/keywords/foveal","display_name":"Foveal","score":0.6003000140190125},{"id":"https://openalex.org/keywords/visual-attention","display_name":"Visual attention","score":0.4848000109195709},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.4715999960899353},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4399999976158142},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.42559999227523804},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.423799991607666},{"id":"https://openalex.org/keywords/visual-search","display_name":"Visual search","score":0.3944000005722046},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.38760000467300415}],"concepts":[{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.6747999787330627},{"id":"https://openalex.org/C30181142","wikidata":"https://www.wikidata.org/wiki/Q865103","display_name":"Foveal","level":3,"score":0.6003000140190125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5752000212669373},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5454999804496765},{"id":"https://openalex.org/C2986089797","wikidata":"https://www.wikidata.org/wiki/Q6501338","display_name":"Visual attention","level":3,"score":0.4848000109195709},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.4715999960899353},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4399999976158142},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.423799991607666},{"id":"https://openalex.org/C158495155","wikidata":"https://www.wikidata.org/wiki/Q2369151","display_name":"Visual search","level":2,"score":0.3944000005722046},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38370001316070557},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3725000023841858},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3677999973297119},{"id":"https://openalex.org/C137878579","wikidata":"https://www.wikidata.org/wiki/Q9636076","display_name":"Joint attention","level":3,"score":0.3544999957084656},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C112356035","wikidata":"https://www.wikidata.org/wiki/Q1672722","display_name":"Iris recognition","level":3,"score":0.34299999475479126},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C2779321571","wikidata":"https://www.wikidata.org/wiki/Q7936605","display_name":"Visual learning","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3359000086784363},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2953000068664551},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.29260000586509705},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.2768999934196472},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25929999351501465},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.2574999928474426},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2563000023365021}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.08442","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08442","pdf_url":"https://arxiv.org/pdf/2510.08442","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.08442","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.08442","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":"pmh:oai:arXiv.org:2510.08442","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08442","pdf_url":"https://arxiv.org/pdf/2510.08442","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4416385168.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Visual":[0],"Reinforcement":[1],"Learning":[2],"(RL)":[3],"agents":[4,26],"must":[5],"learn":[6],"to":[7,27,37,126,132,155,170,184,203],"act":[8],"based":[9,145],"on":[10,33,53,112,146],"high-dimensional":[11],"image":[12],"data":[13],"where":[14],"only":[15],"a":[16,62,70],"small":[17],"fraction":[18],"of":[19],"the":[20,54,75,108,124,129,152,162,167,196,200,207],"pixels":[21],"is":[22,86,116],"task-relevant.":[23],"This":[24,56,115],"forces":[25],"waste":[28],"exploration":[29],"and":[30,39,107,134,143,150,190],"computational":[31],"resources":[32],"irrelevant":[34],"features,":[35],"leading":[36],"sample-inefficient":[38],"unstable":[40],"learning.":[41],"To":[42],"address":[43],"this,":[44],"inspired":[45],"by":[46,69],"human":[47],"visual":[48,59,139],"foveation,":[49],"we":[50],"introduce":[51],"Gaze":[52],"Prize.":[55],"framework":[57],"augments":[58],"RL":[60],"with":[61,177],"learnable":[63],"foveal":[64],"attention":[65,125,168],"mechanism":[66,169],"(Gaze),":[67],"guided":[68],"self-supervised":[71],"signal":[72,164],"derived":[73],"from":[74,195],"agent's":[76],"experience":[77],"pursuing":[78],"higher":[79],"returns":[80],"(the":[81],"Prize).":[82],"Our":[83,180],"key":[84],"insight":[85],"that":[87,122,165,199],"return":[88,148],"differences":[89,149],"reveal":[90],"what":[91],"matters":[92],"most:":[93],"If":[94],"two":[95],"similar":[96,138],"representations":[97,140,173],"produce":[98,171],"different":[99,178],"outcomes,":[100],"their":[101,147],"distinguishing":[102],"features":[103,130],"are":[104],"likely":[105],"task-relevant,":[106],"gaze":[109],"should":[110],"focus":[111],"them":[113],"accordingly.":[114],"realized":[117],"through":[118],"return-guided":[119],"contrastive":[120,157],"learning":[121],"trains":[123],"distinguish":[127],"between":[128],"relevant":[131],"success":[133],"failure.":[135],"We":[136],"group":[137],"into":[141],"positives":[142],"negatives":[144],"use":[151],"resulting":[153],"labels":[154],"construct":[156],"triplets.":[158],"These":[159],"triplets":[160],"provide":[161],"training":[163],"teaches":[166],"distinguishable":[172],"for":[174],"states":[175],"associated":[176],"outcomes.":[179],"method":[181],"achieves":[182],"up":[183],"2.52x":[185],"improvement":[186],"in":[187],"sample":[188],"efficiency":[189],"can":[191],"solve":[192],"challenging":[193],"tasks":[194],"ManiSkill3":[197],"benchmark":[198],"baseline":[201],"fails":[202],"learn,":[204],"without":[205],"modifying":[206],"underlying":[208],"algorithm":[209],"or":[210],"hyperparameters.":[211]},"counts_by_year":[],"updated_date":"2026-03-11T14:59:36.786465","created_date":"2025-10-11T00:00:00"}