{"id":"https://openalex.org/W4413980395","doi":"https://doi.org/10.14778/3749646.3749650","title":"Not Small Enough? SegPQ: A Learned Approach to Compress Product Quantization Codebooks","display_name":"Not Small Enough? SegPQ: A Learned Approach to Compress Product Quantization Codebooks","publication_year":2025,"publication_date":"2025-07-01","ids":{"openalex":"https://openalex.org/W4413980395","doi":"https://doi.org/10.14778/3749646.3749650"},"language":"en","primary_location":{"id":"doi:10.14778/3749646.3749650","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3749646.3749650","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-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/A5046680677","display_name":"Qiyu Liu","orcid":"https://orcid.org/0000-0003-1475-2732"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiyu Liu","raw_affiliation_strings":["Southwest University"],"affiliations":[{"raw_affiliation_string":"Southwest University","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075950865","display_name":"Yanlin Qi","orcid":"https://orcid.org/0000-0001-6572-1093"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanlin Qi","raw_affiliation_strings":["HIT Shenzhen"],"affiliations":[{"raw_affiliation_string":"HIT Shenzhen","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101484322","display_name":"S. Han","orcid":"https://orcid.org/0009-0006-3642-2194"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Siyuan Han","raw_affiliation_strings":["HKUST"],"affiliations":[{"raw_affiliation_string":"HKUST","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103133551","display_name":"Junxiang Peng","orcid":"https://orcid.org/0000-0003-0292-5237"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingshu Peng","raw_affiliation_strings":["ByteDance"],"affiliations":[{"raw_affiliation_string":"ByteDance","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077864179","display_name":"Jin Li","orcid":"https://orcid.org/0000-0003-4844-2572"},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jin Li","raw_affiliation_strings":["Harvard University"],"affiliations":[{"raw_affiliation_string":"Harvard University","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100333468","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0002-4279-3892"},"institutions":[{"id":"https://openalex.org/I4210091106","display_name":"Hindustan Petroleum Corporation Limited (India)","ror":"https://ror.org/00axqt112","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210091106"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["HKUST &amp; HKUST (GZ)"],"affiliations":[{"raw_affiliation_string":"HKUST &amp; HKUST (GZ)","institution_ids":["https://openalex.org/I4210091106"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5046680677"],"corresponding_institution_ids":["https://openalex.org/I142108993"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28816182,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"11","first_page":"3730","last_page":"3743"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9861000180244446,"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/T11269","display_name":"Algorithms and Data Compression","score":0.964900016784668,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/quantization","display_name":"Quantization (signal processing)","score":0.5455120205879211},{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.4945168197154999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49010834097862244},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3602149486541748},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3573550581932068},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2304215431213379}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.5455120205879211},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.4945168197154999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49010834097862244},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3602149486541748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3573550581932068},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2304215431213379}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3749646.3749650","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3749646.3749650","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1576347849","https://openalex.org/W1975684011","https://openalex.org/W1977182282","https://openalex.org/W1987007212","https://openalex.org/W2033836415","https://openalex.org/W2050749090","https://openalex.org/W2077815765","https://openalex.org/W2086179657","https://openalex.org/W2097921974","https://openalex.org/W2124509324","https://openalex.org/W2132234208","https://openalex.org/W2145065594","https://openalex.org/W2152437528","https://openalex.org/W2299467264","https://openalex.org/W2427881153","https://openalex.org/W2464915613","https://openalex.org/W2750779823","https://openalex.org/W2772923331","https://openalex.org/W2912924812","https://openalex.org/W2951434086","https://openalex.org/W2953247561","https://openalex.org/W2963265099","https://openalex.org/W2963469388","https://openalex.org/W2979531022","https://openalex.org/W3037032032","https://openalex.org/W3082379938","https://openalex.org/W3094858795","https://openalex.org/W3103332996","https://openalex.org/W3121516856","https://openalex.org/W3173963616","https://openalex.org/W3198189630","https://openalex.org/W4221058058","https://openalex.org/W4224308101","https://openalex.org/W4230281099","https://openalex.org/W4243780314","https://openalex.org/W4317767740","https://openalex.org/W4390692489","https://openalex.org/W4400118952","https://openalex.org/W4406628711"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2026099691","https://openalex.org/W2377486419","https://openalex.org/W2943202426","https://openalex.org/W2137816434","https://openalex.org/W2163679795","https://openalex.org/W2736714427","https://openalex.org/W2017956276"],"abstract_inverted_index":{"The":[0],"rapid":[1],"advancements":[2],"of":[3,130],"generative":[4],"artificial":[5],"intelligence":[6],"(GenAI)":[7],"have":[8],"recently":[9],"led":[10],"to":[11,38,172,202],"renewed":[12],"attention":[13],"towards":[14],"approximate":[15],"nearest":[16],"neighbor":[17],"(ANN)":[18],"search":[19,96],"and":[20,117],"vector":[21,28,95],"databases":[22],"(VectorDB).":[23],"Among":[24],"various":[25],"ANN":[26],"methodologies,":[27],"quantization":[29,33],"techniques":[30],"like":[31,68],"product":[32],"(PQ)":[34],"are":[35],"widely":[36],"used":[37],"generate":[39],"space-efficient":[40],"representations":[41],"for":[42,61,83,190],"large-scale":[43],"dense":[44],"vectors.":[45],"However,":[46],"the":[47,104,128,147,175],"code-books":[48],"generated":[49,87],"by":[50,88,155,179,200,218],"PQ":[51,90,106,196],"often":[52],"reach":[53],"several":[54],"gigabytes":[55],"in":[56,65],"size,":[57],"making":[58],"them":[59],"impractical":[60],"web-scale,":[62],"high-dimensional":[63],"vectors":[64],"resource-constrained":[66],"environments":[67],"mobile":[69],"devices.":[70],"In":[71],"this":[72],"study,":[73],"we":[74,162],"propose":[75],"SegPQ":[76,102,194],",":[77],"a":[78,109],"simple":[79],"yet":[80],"effective":[81],"framework":[82],"losslessly":[84],"compressing":[85],"codebooks":[86,171],"any":[89],"variants,":[91],"enabling":[92],"efficient":[93],"in-memory":[94],"on":[97,169,185],"devices":[98],"with":[99,125],"limited":[100],"memory.":[101],"represents":[103],"raw":[105],"codewords":[107],"as":[108],"trained":[110],"error-bounded":[111],"piecewise":[112],"linear":[113],"approximation":[114],"model":[115],"(\u03f5-PLA)":[116],"pre-computed":[118],"low-bit":[119],"residuals.":[120],"We":[121],"theoretically":[122],"demonstrate":[123],"that,":[124,189],"high":[126],"probability,":[127],"number":[129],"bits":[131],"per":[132],"compressed":[133,170],"codeword":[134],"is":[135,146],"1.721":[136],"+":[137],"\u2308log":[138],"2":[139],"\u03f5":[140,144],"OPT":[141,145],"\u2309,":[142],"where":[143],"optimal":[148],"error":[149],"parameter":[150],"that":[151],"can":[152],"be":[153],"determined":[154],"data":[156],"characteristics.":[157],"To":[158],"accelerate":[159],"query":[160,166,214],"execution,":[161],"further":[163],"design":[164],"SIMD-aware":[165],"processing":[167,215],"algorithms":[168],"fully":[173],"exploit":[174],"hardware":[176],"parallelism":[177],"offered":[178],"modern":[180],"architectures.":[181],"Extensive":[182],"experimental":[183],"studies":[184],"real":[186],"datasets":[187],"showcase":[188],"1":[191],"billion":[192],"vectors,":[193],"reduces":[195],"codebook":[197],"memory":[198],"consumption":[199],"up":[201],"4.7":[203],"x":[204],"(approx.":[205],"851":[206],"MB":[207],")":[208],"while":[209],"incurring":[210],"only":[211],"3.3%":[212],"additional":[213],"overhead":[216],"caused":[217],"decompression.":[219]},"counts_by_year":[],"updated_date":"2026-02-25T23:00:34.991745","created_date":"2025-10-10T00:00:00"}
