{"id":"https://openalex.org/W7160241460","doi":"https://doi.org/10.1109/wacv61042.2026.00257","title":"NerVast: Compression-Efficient Scaling of Implicit Neural Video Representations via Scene-based Parameter-sharing","display_name":"NerVast: Compression-Efficient Scaling of Implicit Neural Video Representations via Scene-based Parameter-sharing","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160241460","doi":"https://doi.org/10.1109/wacv61042.2026.00257"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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/A5090966470","display_name":"Yunheon Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yunheon Lee","raw_affiliation_strings":["KAIST,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST,Republic of Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029128283","display_name":"Juncheol Ye","orcid":"https://orcid.org/0009-0008-7673-1279"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Juncheol Ye","raw_affiliation_strings":["KAIST,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST,Republic of Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374306","display_name":"Jaehong Kim","orcid":"https://orcid.org/0000-0002-5580-4415"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaehong Kim","raw_affiliation_strings":["Inha University,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inha University,Republic of Korea","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015247369","display_name":"Dongsu Han","orcid":"https://orcid.org/0000-0001-6922-7244"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dongsu Han","raw_affiliation_strings":["KAIST,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST,Republic of Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68112128,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2616","last_page":"2625"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5200999975204468,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5200999975204468,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.13089999556541443,"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/T11448","display_name":"Face recognition and analysis","score":0.04010000079870224,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.4077000021934509},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.3614000082015991},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.361299991607666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3327000141143799},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.32089999318122864},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3003999888896942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6111999750137329},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5741000175476074},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4494999945163727},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.361299991607666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3003999888896942},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28600001335144043},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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":30,"referenced_works":["https://openalex.org/W1972191802","https://openalex.org/W2140199336","https://openalex.org/W2216125271","https://openalex.org/W2505609860","https://openalex.org/W2766328202","https://openalex.org/W2998103904","https://openalex.org/W3031546776","https://openalex.org/W3041133507","https://openalex.org/W3148677604","https://openalex.org/W3174702398","https://openalex.org/W3176828726","https://openalex.org/W3179022832","https://openalex.org/W3203570626","https://openalex.org/W3203806429","https://openalex.org/W3203825070","https://openalex.org/W4200150166","https://openalex.org/W4287756134","https://openalex.org/W4312233465","https://openalex.org/W4312280420","https://openalex.org/W4312493191","https://openalex.org/W4312970380","https://openalex.org/W4313031684","https://openalex.org/W4385682741","https://openalex.org/W4386065502","https://openalex.org/W4386065618","https://openalex.org/W4386071654","https://openalex.org/W4386071704","https://openalex.org/W4409369823","https://openalex.org/W4417281862","https://openalex.org/W7160038549"],"related_works":[],"abstract_inverted_index":{"Implicit":[0],"neural":[1],"representation":[2,10,135],"(INR)":[3],"has":[4],"emerged":[5],"as":[6],"a":[7,94,119,165],"new":[8,95],"data":[9,72],"for":[11,34,49,98,134],"compressing":[12],"videos":[13],"and":[14,51,58,111,161],"now":[15],"shows":[16],"on-par":[17],"performance":[18],"with":[19,214],"the":[20,27,67,108,130,138,149,169,196,215],"conventional":[21],"codecs.":[22],"The":[23],"next":[24],"challenge":[25],"in":[26,123,164,190,206,212],"field":[28],"is":[29,121,204],"to":[30,47,64,84,153,195],"make":[31],"INR":[32,45,116,125,198],"scalable":[33],"its":[35],"practical":[36],"use.":[37],"Existing":[38],"works":[39],"realize":[40],"this":[41,89],"by":[42,136,173],"utilizing":[43],"small":[44,115],"models":[46,141],"scale":[48],"long":[50],"high-resolution":[52],"video,":[53],"which":[54,82],"achieves":[55],"better":[56,185,205],"encoding":[57,74,96,207],"decoding":[59,178],"speeds.":[60],"However,":[61],"they":[62],"fail":[63],"fully":[65],"exploit":[66],"temporal":[68],"nature":[69],"of":[70,114],"video":[71,99,120],"when":[73],"it":[75,156],"into":[76],"multiple":[77],"separate":[78,124],"INRs":[79],"across":[80],"time,":[81],"leads":[83],"sub-optimal":[85],"compression":[86,103,186],"efficiency.":[87],"In":[88,200],"work,":[90],"we":[91],"propose":[92],"NerVast,":[93],"scheme":[97],"INR,":[100],"that":[101,167],"improves":[102],"efficiency":[104],"while":[105],"still":[106],"enjoying":[107],"low":[109],"computation":[110],"transfer":[112],"costs":[113],"models.":[117,199],"When":[118],"represented":[122],"segments,":[126],"NerVast":[127,147,183,203],"effectively":[128],"reduces":[129],"total":[131],"volume":[132],"required":[133],"sharing":[137],"parameters":[139,152,163,191],"between":[140],"during":[142],"encoding.":[143],"Without":[144],"expensive":[145],"training,":[146],"selects":[148],"most":[150],"efficient":[151],"share.":[154],"Then":[155],"jointly":[157],"trains":[158],"both":[159],"shared":[160],"non-shared":[162],"way":[166],"minimizes":[168],"quality":[170,208],"drop":[171],"imposed":[172],"sharing.":[174],"While":[175],"maintaining":[176],"real-time":[177],"speed":[179],"(>":[180],"30":[181],"fps),":[182],"provides":[184],"(39.9":[187],"%":[188],"reduction":[189],"on":[192],"average)":[193],"compared":[194],"compute-efficient":[197],"other":[201],"words,":[202],"(1.57":[209],"dB":[210],"higher":[211],"PSNR)":[213],"same":[216],"bitrate.":[217]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-06T00:00:00"}