{"id":"https://openalex.org/W4391468321","doi":"https://doi.org/10.1109/m2vip58386.2023.10413385","title":"Gaze Estimation with Multi-scale Attention-based Convolutional Neural Networks","display_name":"Gaze Estimation with Multi-scale Attention-based Convolutional Neural Networks","publication_year":2023,"publication_date":"2023-11-21","ids":{"openalex":"https://openalex.org/W4391468321","doi":"https://doi.org/10.1109/m2vip58386.2023.10413385"},"language":"en","primary_location":{"id":"doi:10.1109/m2vip58386.2023.10413385","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/m2vip58386.2023.10413385","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100320684","display_name":"Yuanyuan Zhang","orcid":"https://orcid.org/0000-0003-1714-9736"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanyuan Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Tianjin University of Technology,Tianjin,China,300384"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Tianjin University of Technology,Tianjin,China,300384","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336774","display_name":"Jing Li","orcid":"https://orcid.org/0000-0001-6566-1406"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Li","raw_affiliation_strings":["School of Computer Science and Engineering, Tianjin University of Technology,Tianjin,China,300384"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Tianjin University of Technology,Tianjin,China,300384","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109973657","display_name":"Gaoxiang Ouyang","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaoxiang Ouyang","raw_affiliation_strings":["Neuroscience and Learning, Beijing Normal University,State Key Laboratory of Cognitive,Beijing,China,100875"],"affiliations":[{"raw_affiliation_string":"Neuroscience and Learning, Beijing Normal University,State Key Laboratory of Cognitive,Beijing,China,100875","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100320684"],"corresponding_institution_ids":["https://openalex.org/I136765683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19905654,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9998999834060669,"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":0.9998999834060669,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9919999837875366,"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.9736999869346619,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.9340155124664307},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7883541584014893},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.784747838973999},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7065510153770447},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6035519242286682},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.570182204246521},{"id":"https://openalex.org/keywords/head","display_name":"Head (geology)","score":0.4904458224773407},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46165192127227783},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43127375841140747},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3782973885536194},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06889691948890686},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.05579701066017151}],"concepts":[{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.9340155124664307},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7883541584014893},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.784747838973999},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7065510153770447},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6035519242286682},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.570182204246521},{"id":"https://openalex.org/C2780312720","wikidata":"https://www.wikidata.org/wiki/Q5689100","display_name":"Head (geology)","level":2,"score":0.4904458224773407},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46165192127227783},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43127375841140747},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3782973885536194},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06889691948890686},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.05579701066017151},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/m2vip58386.2023.10413385","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/m2vip58386.2023.10413385","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1971652879","https://openalex.org/W2136773441","https://openalex.org/W2194775991","https://openalex.org/W2557669140","https://openalex.org/W2758436589","https://openalex.org/W2778474385","https://openalex.org/W2799064164","https://openalex.org/W2884585870","https://openalex.org/W2895535699","https://openalex.org/W2921627265","https://openalex.org/W2921653116","https://openalex.org/W2928165649","https://openalex.org/W2982035692","https://openalex.org/W2986102820","https://openalex.org/W2998355285","https://openalex.org/W3013897564","https://openalex.org/W3157282359","https://openalex.org/W3179103990","https://openalex.org/W3187383522","https://openalex.org/W4210889190","https://openalex.org/W4210936114","https://openalex.org/W4224929143","https://openalex.org/W4225646287","https://openalex.org/W4292787489","https://openalex.org/W4307085872","https://openalex.org/W4319300355","https://openalex.org/W4385804914","https://openalex.org/W4386883051"],"related_works":["https://openalex.org/W1880689012","https://openalex.org/W3014378845","https://openalex.org/W4240909707","https://openalex.org/W2059546927","https://openalex.org/W3207760378","https://openalex.org/W4386072035","https://openalex.org/W2562758970","https://openalex.org/W1986970529","https://openalex.org/W1563178652","https://openalex.org/W2947492009"],"abstract_inverted_index":{"Gaze":[0],"estimation":[1,41,73],"has":[2],"gained":[3],"increasing":[4],"attention":[5],"due":[6,20],"to":[7,21,47],"its":[8],"widespread":[9],"applications.":[10],"In":[11],"real-world":[12],"unconstrained":[13],"environments,":[14],"the":[15,49,60,82,91],"performance":[16],"is":[17],"still":[18],"unstable":[19],"large":[22],"variations":[23],"of":[24,51,62,85],"head":[25,52],"posture":[26],"and":[27,55,88,94],"environmental":[28],"conditions":[29],"such":[30],"as":[31],"illumination":[32],"changes.":[33],"This":[34],"paper":[35],"proposes":[36],"a":[37],"novel":[38],"appearancebased":[39],"gaze":[40,72],"method":[42,65,80],"by":[43,66],"extracting":[44],"multi-scale":[45],"features":[46],"solve":[48],"problems":[50],"pose":[53],"changes":[54],"lighting":[56],"effects.":[57],"We":[58],"demonstrate":[59],"effectiveness":[61],"our":[63,79],"proposed":[64],"conducting":[67],"experiments":[68],"on":[69,90],"three":[70],"popular":[71],"datasets.":[74],"Experimental":[75],"results":[76],"show":[77],"that":[78],"achieves":[81],"prediction":[83],"errors":[84],"3.47\u00b0,":[86],"10.57\u00b0,":[87],"6.95\u00b0":[89],"MPIIFaceGaze,":[92],"Gaze360":[93],"RT-GENE":[95],"datasets,":[96],"respectively.":[97]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}