{"id":"https://openalex.org/W4308235897","doi":"https://doi.org/10.1109/icip46576.2022.9898025","title":"Efficient Transformer with Locally Shared Attention for Video Quality Assessment","display_name":"Efficient Transformer with Locally Shared Attention for Video Quality Assessment","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308235897","doi":"https://doi.org/10.1109/icip46576.2022.9898025"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9898025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9898025","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5052816488","display_name":"Junyong You","orcid":"https://orcid.org/0000-0002-4288-5244"},"institutions":[{"id":"https://openalex.org/I4210107808","display_name":"NORCE Norwegian Research Centre","ror":"https://ror.org/02gagpf75","country_code":"NO","type":"facility","lineage":["https://openalex.org/I4210107808"]}],"countries":["NO"],"is_corresponding":true,"raw_author_name":"Junyong You","raw_affiliation_strings":["Norwegian Research Centre (NORCE),Bergen,Norway","Norwegian Research Centre (NORCE), Bergen, Norway"],"affiliations":[{"raw_affiliation_string":"Norwegian Research Centre (NORCE),Bergen,Norway","institution_ids":["https://openalex.org/I4210107808"]},{"raw_affiliation_string":"Norwegian Research Centre (NORCE), Bergen, Norway","institution_ids":["https://openalex.org/I4210107808"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100754379","display_name":"Yuan Lin","orcid":"https://orcid.org/0000-0002-6974-8856"},"institutions":[{"id":"https://openalex.org/I2800207870","display_name":"H\u00f8yskolen Kristiania","ror":"https://ror.org/03gss5916","country_code":"NO","type":"education","lineage":["https://openalex.org/I2800207870"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Yuan Lin","raw_affiliation_strings":["Kristiania University College,Bergen,Norway","Kristiania University College, Bergen, Norway"],"affiliations":[{"raw_affiliation_string":"Kristiania University College,Bergen,Norway","institution_ids":["https://openalex.org/I2800207870"]},{"raw_affiliation_string":"Kristiania University College, Bergen, Norway","institution_ids":["https://openalex.org/I2800207870"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5052816488"],"corresponding_institution_ids":["https://openalex.org/I4210107808"],"apc_list":null,"apc_paid":null,"fwci":0.06,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.28753743,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"1","issue":null,"first_page":"356","last_page":"360"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9998999834060669,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9998999834060669,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9966999888420105,"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/computer-science","display_name":"Computer science","score":0.8248920440673828},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6949033141136169},{"id":"https://openalex.org/keywords/clips","display_name":"CLIPS","score":0.6302125453948975},{"id":"https://openalex.org/keywords/video-quality","display_name":"Video quality","score":0.599361002445221},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5746768712997437},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.500241756439209},{"id":"https://openalex.org/keywords/subjective-video-quality","display_name":"Subjective video quality","score":0.4629583954811096},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3764496445655823},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3671601414680481},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3631021976470947},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.357366681098938},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3260955810546875},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.10957923531532288},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.07706201076507568}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8248920440673828},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6949033141136169},{"id":"https://openalex.org/C2778739407","wikidata":"https://www.wikidata.org/wiki/Q165372","display_name":"CLIPS","level":2,"score":0.6302125453948975},{"id":"https://openalex.org/C103910844","wikidata":"https://www.wikidata.org/wiki/Q2631256","display_name":"Video quality","level":3,"score":0.599361002445221},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5746768712997437},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.500241756439209},{"id":"https://openalex.org/C114227958","wikidata":"https://www.wikidata.org/wiki/Q7631422","display_name":"Subjective video quality","level":4,"score":0.4629583954811096},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3764496445655823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3671601414680481},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3631021976470947},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.357366681098938},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3260955810546875},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10957923531532288},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.07706201076507568},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9898025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9898025","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5199999809265137,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2017175959","https://openalex.org/W2036502252","https://openalex.org/W2040667057","https://openalex.org/W2048042940","https://openalex.org/W2144764737","https://openalex.org/W2194363988","https://openalex.org/W2368744241","https://openalex.org/W2611434713","https://openalex.org/W2886541144","https://openalex.org/W2904679285","https://openalex.org/W2919115771","https://openalex.org/W2939995367","https://openalex.org/W2950154603","https://openalex.org/W2965644659","https://openalex.org/W2970478035","https://openalex.org/W2999295593","https://openalex.org/W3002992380","https://openalex.org/W3030701471","https://openalex.org/W3035595647","https://openalex.org/W3091249416","https://openalex.org/W3093431261","https://openalex.org/W3099047215","https://openalex.org/W3103745061","https://openalex.org/W3117922782","https://openalex.org/W3138516171","https://openalex.org/W3159397114","https://openalex.org/W3199104506","https://openalex.org/W4200167653","https://openalex.org/W6639921068","https://openalex.org/W6739901393","https://openalex.org/W6755207826","https://openalex.org/W6771626834","https://openalex.org/W6784333009","https://openalex.org/W6798335185"],"related_works":["https://openalex.org/W4388138958","https://openalex.org/W1996919562","https://openalex.org/W2046455617","https://openalex.org/W4390244677","https://openalex.org/W2809031010","https://openalex.org/W2061696964","https://openalex.org/W2546999347","https://openalex.org/W3194854785","https://openalex.org/W3014439586","https://openalex.org/W1497453854"],"abstract_inverted_index":{"Transformer":[0,26,66,123],"has":[1],"shown":[2],"outstanding":[3],"performance":[4,140],"in":[5,48,101,141],"time-series":[6],"data":[7],"processing,":[8],"which":[9,71],"can":[10,153],"definitely":[11],"facilitate":[12],"quality":[13,50,90,96,126],"assessment":[14,51],"of":[15,25,42],"video":[16,33,46,49,81],"sequences.":[17,34],"However,":[18],"the":[19,74],"quadratic":[20],"time":[21,75],"and":[22,76],"memory":[23,77],"complexities":[24],"potentially":[27],"impede":[28],"its":[29],"application":[30],"to":[31],"long":[32,80],"In":[35],"this":[36],"work,":[37],"we":[38],"study":[39],"a":[40,102,107,121,135],"mechanism":[41],"sharing":[43],"attention":[44,63],"across":[45,114],"clips":[47,116],"(VQA)":[52],"scenario.":[53],"Consequently,":[54],"an":[55,94],"efficient":[56],"architecture":[57,137],"based":[58],"on":[59,98,146],"integrating":[60],"shared":[61,108],"multi-head":[62],"(MHA)":[64],"into":[65,86,120],"is":[67,83],"proposed":[68,132],"for":[69,125],"VQA,":[70],"greatly":[72],"ease":[73],"complexities.":[78],"A":[79],"sequence":[82,129],"first":[84],"divided":[85],"individual":[87],"clips.":[88],"The":[89,111,131,150],"features":[91,113],"derived":[92],"by":[93,106],"image":[95],"model":[97,133],"each":[99],"frame":[100],"clip":[103],"are":[104,117],"aggregated":[105,112],"MHA":[109],"layer.":[110],"all":[115],"then":[118],"fed":[119],"global":[122],"encoder":[124],"prediction":[127],"at":[128,156],"level.":[130],"with":[134],"lightweight":[136],"demonstrates":[138],"promising":[139],"no-reference":[142],"VQA":[143],"(NR-VQA)":[144],"modelling":[145],"publicly":[147],"available":[148],"databases.":[149],"source":[151],"code":[152],"be":[154],"found":[155],"https://github.com/junyongyou/lagt_vqa.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}