{"id":"https://openalex.org/W4403791691","doi":"https://doi.org/10.1145/3664647.3680811","title":"Compacter: A Lightweight Transformer for Image Restoration","display_name":"Compacter: A Lightweight Transformer for Image Restoration","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791691","doi":"https://doi.org/10.1145/3664647.3680811"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3680811","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680811","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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/A5042499646","display_name":"Zhijian Wu","orcid":"https://orcid.org/0000-0002-3040-3287"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhijian Wu","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088380345","display_name":"Jun Li","orcid":"https://orcid.org/0000-0002-9781-8954"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Li","raw_affiliation_strings":["Nanjing Normal University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing Normal University, Nanjing, China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108919112","display_name":"Yang Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Hu","raw_affiliation_strings":["New York University, Brooklyn, USA"],"affiliations":[{"raw_affiliation_string":"New York University, Brooklyn, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101940253","display_name":"Dingjiang Huang","orcid":"https://orcid.org/0000-0002-0144-7344"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dingjiang Huang","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5042499646"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19216639,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3094","last_page":"3103"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","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/T11105","display_name":"Advanced Image Processing Techniques","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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994999766349792,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9988999962806702,"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/transformer","display_name":"Transformer","score":0.6081034541130066},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5451071858406067},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.5428587794303894},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3353158235549927},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3023868799209595},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.21841999888420105},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.20233482122421265},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18215569853782654},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.09693396091461182}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6081034541130066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5451071858406067},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.5428587794303894},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3353158235549927},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3023868799209595},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.21841999888420105},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.20233482122421265},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18215569853782654},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.09693396091461182}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3680811","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680811","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1912194039","https://openalex.org/W1930824406","https://openalex.org/W2047920195","https://openalex.org/W2110158442","https://openalex.org/W2121058967","https://openalex.org/W2121927366","https://openalex.org/W2192954843","https://openalex.org/W2476548250","https://openalex.org/W2556068545","https://openalex.org/W2559264300","https://openalex.org/W2580458810","https://openalex.org/W2739757502","https://openalex.org/W2741137940","https://openalex.org/W2866634454","https://openalex.org/W2913360047","https://openalex.org/W2950335850","https://openalex.org/W2954930822","https://openalex.org/W2963017889","https://openalex.org/W2964101377","https://openalex.org/W2980047233","https://openalex.org/W2981718299","https://openalex.org/W3034539499","https://openalex.org/W3035326127","https://openalex.org/W3039817009","https://openalex.org/W3101659800","https://openalex.org/W3104028135","https://openalex.org/W3120540810","https://openalex.org/W3121661546","https://openalex.org/W3134765225","https://openalex.org/W3170026688","https://openalex.org/W3171125843","https://openalex.org/W4224130636","https://openalex.org/W4225576932","https://openalex.org/W4295136549","https://openalex.org/W4310278871","https://openalex.org/W4312784114","https://openalex.org/W4379806295","https://openalex.org/W4382240242","https://openalex.org/W4386075509","https://openalex.org/W4386075513","https://openalex.org/W4386075704","https://openalex.org/W4386076602","https://openalex.org/W4389977645","https://openalex.org/W4390872882","https://openalex.org/W4390872911","https://openalex.org/W4402753660","https://openalex.org/W6677973867"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W1598401975","https://openalex.org/W2974904990","https://openalex.org/W2365681766","https://openalex.org/W2393963626"],"abstract_inverted_index":{"Although":[0],"deep":[1],"learning-based":[2],"methods":[3],"have":[4],"made":[5],"significant":[6],"advances":[7],"in":[8],"the":[9,44,130],"field":[10],"of":[11,46,150],"image":[12,35],"restoration":[13,36],"(IR),":[14],"they":[15],"often":[16],"suffer":[17],"from":[18,93],"excessive":[19],"model":[20,75],"parameters.":[21,157],"To":[22],"tackle":[23],"this":[24,26],"problem,":[25],"work":[27],"proposes":[28],"a":[29,56,103,121,148],"compact":[30],"Transformer":[31],"(Compacter)":[32],"for":[33,135,147],"lightweight":[34,151],"by":[37],"making":[38],"several":[39],"key":[40],"designs.":[41],"We":[42],"employ":[43],"concepts":[45],"projection":[47,66],"sharing,":[48],"adaptive":[49,81],"interaction,":[50],"and":[51,71,77,89],"heterogeneous":[52,112],"aggregation":[53],"to":[54,67,73,87,110,127],"develop":[55],"novel":[57],"Compact":[58],"Adaptive":[59],"Self-Attention":[60],"(CASA).":[61],"Specifically,":[62],"CASA":[63],"utilizes":[64],"shared":[65],"generate":[68],"Query,":[69],"Key,":[70],"Value":[72,109],"simultaneously":[74],"spatial":[76],"channel-wise":[78],"self-attention.":[79],"The":[80],"interaction":[82],"process":[83],"is":[84,106],"then":[85],"used":[86],"propagate":[88],"integrate":[90],"global":[91],"information":[92],"two":[94],"different":[95],"dimensions,":[96],"thus":[97],"enabling":[98,115],"omnidirectional":[99],"relational":[100],"interaction.":[101],"Finally,":[102],"depth-wise":[104],"convolution":[105],"incorporated":[107],"on":[108],"complement":[111],"local":[113],"information,":[114],"global-local":[116],"coupling.":[117],"Moreover,":[118],"we":[119],"propose":[120],"Dual":[122],"Selective":[123],"Gated":[124],"Module":[125],"(DSGM)":[126],"dynamically":[128],"encapsulate":[129],"globality":[131],"into":[132],"each":[133],"pixel":[134],"context-adaptive":[136],"aggregation.":[137],"Extensive":[138],"experiments":[139],"demonstrate":[140],"that":[141],"our":[142],"Compacter":[143],"achieves":[144],"state-of-the-art":[145],"performance":[146],"variety":[149],"IR":[152],"tasks":[153],"with":[154],"approximately":[155],"400K":[156]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
