{"id":"https://openalex.org/W4403792277","doi":"https://doi.org/10.1145/3664647.3681050","title":"QVD: Post-training Quantization for Video Diffusion Models","display_name":"QVD: Post-training Quantization for Video Diffusion Models","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403792277","doi":"https://doi.org/10.1145/3664647.3681050"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681050","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681050","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":null,"display_name":"Shilong Tian","orcid":"https://orcid.org/0009-0009-9482-2577"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shilong Tian","raw_affiliation_strings":["Beihang University & Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-9482-2577","affiliations":[{"raw_affiliation_string":"Beihang University & Meituan, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hong Chen","orcid":"https://orcid.org/0009-0001-9658-0593"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Chen","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-9658-0593","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056240323","display_name":"Chengtao Lv","orcid":"https://orcid.org/0000-0001-9599-6557"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengtao Lv","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9599-6557","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100345645","display_name":"Yu Liu","orcid":"https://orcid.org/0000-0001-7599-612X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Liu","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7599-612X","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039703932","display_name":"Jinyang Guo","orcid":"https://orcid.org/0000-0003-1956-3367"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinyang Guo","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1956-3367","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024067284","display_name":"Xianglong Liu","orcid":"https://orcid.org/0000-0001-8425-4195"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianglong Liu","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8425-4195","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shengxi Li","orcid":"https://orcid.org/0009-0000-0994-5631"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengxi Li","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-0994-5631","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hao Yang","orcid":"https://orcid.org/0009-0001-0871-7263"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Yang","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-0871-7263","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]},{"author_position":"last","author":{"id":null,"display_name":"Tao Xie","orcid":"https://orcid.org/0009-0006-1524-9505"},"institutions":[{"id":"https://openalex.org/I4210087373","display_name":"Meizu (China)","ror":"https://ror.org/0067g4302","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210087373"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Xie","raw_affiliation_strings":["Meituan, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-1524-9505","affiliations":[{"raw_affiliation_string":"Meituan, Beijing, China","institution_ids":["https://openalex.org/I4210087373"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8749,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75583406,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"10572","last_page":"10581"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9958000183105469,"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":0.9958000183105469,"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":0.9939000010490417,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9929999709129333,"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":0.6822510957717896},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.6247488260269165},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4935702085494995},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4617166221141815},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3810652494430542},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3379727900028229},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07653981447219849}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6822510957717896},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6247488260269165},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4935702085494995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4617166221141815},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3810652494430542},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3379727900028229},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07653981447219849},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681050","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681050","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2765811365","https://openalex.org/W2803549871","https://openalex.org/W2963122961","https://openalex.org/W2966421984","https://openalex.org/W2998218113","https://openalex.org/W3178284600","https://openalex.org/W4212774754","https://openalex.org/W4281661987","https://openalex.org/W4286744023","https://openalex.org/W4308245637","https://openalex.org/W4310695675","https://openalex.org/W4312933868","https://openalex.org/W4386065704","https://openalex.org/W4386071831","https://openalex.org/W4386071957","https://openalex.org/W4386076323","https://openalex.org/W4386758232","https://openalex.org/W4387968628","https://openalex.org/W4390873135","https://openalex.org/W4390874074","https://openalex.org/W4390874168","https://openalex.org/W4390874580","https://openalex.org/W4402683778","https://openalex.org/W4402715871","https://openalex.org/W6810793953"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Recently,":[0],"video":[1,19,91,122,155],"diffusion":[2,92,123],"models":[3],"(VDMs)":[4],"have":[5],"garnered":[6],"significant":[7,82],"attention":[8],"due":[9],"to":[10,52,170],"their":[11,42],"notable":[12],"advancements":[13],"in":[14,34,87,95,195,215],"generating":[15],"coherent":[16],"and":[17,37,56,85,104,186],"realistic":[18],"content.":[20],"However,":[21],"processing":[22],"multiple":[23],"frame":[24,74],"features":[25],"concurrently,":[26],"coupled":[27],"with":[28],"the":[29,66,88,106,116,130,143,161,172,190,210],"considerable":[30],"model":[31],"size,":[32],"results":[33],"high":[35,144],"latency":[36],"extensive":[38],"memory":[39,54],"consumption,":[40],"hindering":[41],"broader":[43],"application.":[44],"Post-training":[45],"quantization":[46,99,175],"(PTQ)":[47],"is":[48],"an":[49],"effective":[50],"technique":[51],"reduce":[53],"footprint":[55],"improve":[57,171],"computational":[58],"efficiency.":[59],"Unlike":[60],"image":[61],"diffusion,":[62],"we":[63,80,114,128,159,202],"observe":[64],"that":[65],"temporal":[67,139,151],"features,":[68,75,140,148],"which":[69,141,168],"are":[70],"integrated":[71],"into":[72],"all":[73,154],"exhibit":[76],"pronounced":[77],"skewness.":[78],"Furthermore,":[79],"investigate":[81],"inter-channel":[83],"disparities":[84],"asymmetries":[86],"activation":[89],"of":[90,98,108,146,174,192,197],"models,":[93,124,184],"resulting":[94],"low":[96],"coverage":[97,173],"levels":[100,176],"by":[101,213],"individual":[102,178],"channels":[103],"increasing":[105],"challenge":[107],"quantization.":[109],"To":[110],"address":[111],"these":[112],"issues,":[113],"introduce":[115],"first":[117],"PTQ":[118],"strategy":[119],"tailored":[120],"for":[121,138,153],"dubbed":[125],"QVD.":[126],"Specifically,":[127],"propose":[129],"High":[131],"Temporal":[132],"Discriminability":[133],"Quantization":[134],"(HTDQ)":[135],"method,":[136],"designed":[137],"retains":[142],"discriminability":[145],"quantized":[147],"providing":[149],"precise":[150],"guidance":[152],"frames.":[156],"In":[157,200],"addition,":[158],"present":[160],"Scattered":[162],"Channel":[163],"Range":[164],"Integration":[165],"(SCRI)":[166],"method":[167],"aims":[169],"across":[177,182],"channels.":[179],"Experimental":[180],"validations":[181],"various":[183],"datasets,":[185],"bit-width":[187],"settings":[188],"demonstrate":[189],"effectiveness":[191],"our":[193],"QVD":[194],"terms":[196],"diverse":[198],"metrics.":[199],"particular,":[201],"achieve":[203],"near-lossless":[204],"performance":[205],"degradation":[206],"on":[207],"W8A8,":[208],"outperforming":[209],"current":[211],"methods":[212],"205.12":[214],"FVD.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}