{"id":"https://openalex.org/W4385764452","doi":"https://doi.org/10.24963/ijcai.2023/121","title":"On Efficient Transformer-Based Image Pre-training for Low-Level Vision","display_name":"On Efficient Transformer-Based Image Pre-training for Low-Level Vision","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4385764452","doi":"https://doi.org/10.24963/ijcai.2023/121"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2023/121","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/121","pdf_url":"https://www.ijcai.org/proceedings/2023/0121.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2023/0121.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100336606","display_name":"Wenbo Li","orcid":"https://orcid.org/0000-0003-4604-778X"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wenbo Li","raw_affiliation_strings":["The Chinese University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100719897","display_name":"Xin Lu","orcid":"https://orcid.org/0000-0001-6486-3460"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin Lu","raw_affiliation_strings":["Deeproute.ai"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Deeproute.ai","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034240662","display_name":"Shengju Qian","orcid":"https://orcid.org/0000-0002-3386-0336"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Shengju Qian","raw_affiliation_strings":["The Chinese University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084252480","display_name":"Jiangbo Lu","orcid":"https://orcid.org/0000-0002-0048-3140"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiangbo Lu","raw_affiliation_strings":["SmartMore Corporation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SmartMore Corporation","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":56,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1089","last_page":"1097"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998000264167786,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998000264167786,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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":0.9965999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7814321517944336},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6953297853469849},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5628899335861206},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5506908893585205},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5099962949752808},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.43499237298965454},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.42087966203689575},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3980615735054016},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33074408769607544},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.15316471457481384},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10464149713516235}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7814321517944336},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6953297853469849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5628899335861206},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5506908893585205},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5099962949752808},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.43499237298965454},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.42087966203689575},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3980615735054016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33074408769607544},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.15316471457481384},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10464149713516235},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2023/121","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/121","pdf_url":"https://www.ijcai.org/proceedings/2023/0121.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2023/121","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/121","pdf_url":"https://www.ijcai.org/proceedings/2023/0121.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.4300000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334009","display_name":"Guangdong Provincial Pearl River Talents Program","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4385764452.pdf"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W1920328734","https://openalex.org/W1930824406","https://openalex.org/W2056370875","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2112552549","https://openalex.org/W2147800946","https://openalex.org/W2209874411","https://openalex.org/W2412782625","https://openalex.org/W2466666260","https://openalex.org/W2508457857","https://openalex.org/W2509784253","https://openalex.org/W2734663976","https://openalex.org/W2740982616","https://openalex.org/W2741137940","https://openalex.org/W2780930362","https://openalex.org/W2799269579","https://openalex.org/W2866634454","https://openalex.org/W2884068670","https://openalex.org/W2891158090","https://openalex.org/W2896457183","https://openalex.org/W2912435603","https://openalex.org/W2913360047","https://openalex.org/W2930755307","https://openalex.org/W2954930822","https://openalex.org/W2962843773","https://openalex.org/W2963703197","https://openalex.org/W2963878020","https://openalex.org/W2971719842","https://openalex.org/W3023003829","https://openalex.org/W3035250394","https://openalex.org/W3081639259","https://openalex.org/W3094502228","https://openalex.org/W3104725225","https://openalex.org/W3109319753","https://openalex.org/W3127775241","https://openalex.org/W3131500599","https://openalex.org/W3138516171","https://openalex.org/W3171125843","https://openalex.org/W3174531399","https://openalex.org/W3175544090","https://openalex.org/W3196057788","https://openalex.org/W3207918547","https://openalex.org/W3212228063","https://openalex.org/W4221163966","https://openalex.org/W4225672218","https://openalex.org/W4226440503","https://openalex.org/W4239072543","https://openalex.org/W4287022992","https://openalex.org/W4287682995","https://openalex.org/W4292779060","https://openalex.org/W4306979388","https://openalex.org/W4312812783","https://openalex.org/W4313156423","https://openalex.org/W4385245566","https://openalex.org/W4386083034","https://openalex.org/W6759363029","https://openalex.org/W6791858558","https://openalex.org/W6803771590","https://openalex.org/W6863071542","https://openalex.org/W6863994431","https://openalex.org/W6864014924"],"related_works":["https://openalex.org/W4394050964","https://openalex.org/W2551249631","https://openalex.org/W1564680838","https://openalex.org/W3098003361","https://openalex.org/W2003125260","https://openalex.org/W2060591604","https://openalex.org/W1992291644","https://openalex.org/W2166791242","https://openalex.org/W2585162246","https://openalex.org/W1934413089"],"abstract_inverted_index":{"Pre-training":[0],"has":[1],"marked":[2],"numerous":[3],"state":[4],"of":[5,46,53,111],"the":[6,44,147],"arts":[7],"in":[8,24,73,86,100,103],"high-level":[9],"computer":[10],"vision,":[11],"while":[12,93],"few":[13],"attempts":[14],"have":[15],"ever":[16],"been":[17],"made":[18],"to":[19,83,130],"investigate":[20],"how":[21],"pre-training":[22,34,68,78,94,116],"acts":[23],"image":[25],"processing":[26],"systems.":[27],"In":[28],"this":[29],"paper,":[30],"we":[31,48,107,126,149],"tailor":[32],"transformer-based":[33],"regimes":[35],"that":[36,57,67,114],"boost":[37],"various":[38],"low-level":[39,74,156],"tasks.":[40,75,157],"To":[41],"comprehensively":[42],"diagnose":[43],"influence":[45],"pre-training,":[47,112],"design":[49],"a":[50],"whole":[51],"set":[52],"principled":[54],"evaluation":[55],"tools":[56],"uncover":[58],"its":[59],"effects":[60],"on":[61,146],"internal":[62,97],"representations.":[63],"The":[64],"observations":[65],"demonstrate":[66],"plays":[69],"strikingly":[70],"different":[71,109],"roles":[72],"For":[76],"example,":[77],"introduces":[79],"more":[80,118],"local":[81],"information":[82],"intermediate":[84],"layers":[85],"super-resolution":[87],"(SR),":[88],"yielding":[89],"significant":[90],"performance":[91],"gains,":[92],"hardly":[95],"affects":[96],"feature":[98],"representations":[99],"denoising,":[101],"resulting":[102],"limited":[104],"gains.":[105],"Further,":[106],"explore":[108],"methods":[110],"revealing":[113],"multi-related-task":[115],"is":[117],"effective":[119],"and":[120,134,143],"data-efficient":[121],"than":[122],"other":[123],"alternatives.":[124],"Finally,":[125],"extend":[127],"our":[128],"study":[129],"varying":[131],"data":[132],"scales":[133],"model":[135],"sizes,":[136],"as":[137,139],"well":[138],"comparisons":[140],"between":[141],"transformers":[142],"CNNs.":[144],"Based":[145],"study,":[148],"successfully":[150],"develop":[151],"state-of-the-art":[152],"models":[153],"for":[154],"multiple":[155]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":6}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
