{"id":"https://openalex.org/W7161726260","doi":"https://doi.org/10.48550/arxiv.2605.16901","title":"CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model","display_name":"CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model","publication_year":2026,"publication_date":"2026-05-16","ids":{"openalex":"https://openalex.org/W7161726260","doi":"https://doi.org/10.48550/arxiv.2605.16901"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16901","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16901","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.16901","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136501406","display_name":"Houji Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Houji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136478002","display_name":"Jiangyong Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Jiangyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136496756","display_name":"Jun Li","orcid":"https://orcid.org/0000-0002-0148-0419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136482310","display_name":"Dawei Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Dawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5853999853134155,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.5853999853134155,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.08030000329017639,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.05570000037550926,"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/quantization","display_name":"Quantization (signal processing)","score":0.7059999704360962},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4023999869823456},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.3937999904155731},{"id":"https://openalex.org/keywords/unobservable","display_name":"Unobservable","score":0.39070001244544983},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.3483999967575073},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.34439998865127563}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.7059999704360962},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6909000277519226},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5134999752044678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4595000147819519},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45809999108314514},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C2780695315","wikidata":"https://www.wikidata.org/wiki/Q3799040","display_name":"Unobservable","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.3483999967575073},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26409998536109924},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25949999690055847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16901","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16901","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.16901","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16901","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Segment":[0],"Anything":[1],"Models":[2],"(SAMs)":[3],"are":[4],"extensively":[5],"used":[6,36],"in":[7,53,75,153],"computer":[8],"vision":[9],"for":[10,38,81,125],"universal":[11],"image":[12],"segmentation,":[13],"but":[14],"deploying":[15],"them":[16],"on":[17,198],"resource-constrained":[18],"devices":[19],"is":[20,33,79],"challenging":[21],"due":[22],"to":[23,48,128,145,150,187],"their":[24],"high":[25],"computational":[26],"and":[27,41,89,95,110,173,195,200],"memory":[28],"demands.":[29],"Post-Training":[30],"Quantization":[31],"(PTQ)":[32],"a":[34,87,120,158],"widely":[35],"technique":[37],"model":[39],"compression":[40],"acceleration.":[42],"However,":[43],"existing":[44,191],"PTQ":[45],"methods":[46,192],"fail":[47],"consider":[49],"the":[50,54,62,72,76,103],"cross-attention":[51],"architecture":[52],"SAM":[55,184],"decoder.":[56],"This":[57],"degradation":[58],"primarily":[59],"stems":[60],"from":[61,143],"unique":[63],"challenges":[64],"posed":[65],"by":[66,193],"SAMs:":[67],"(1)":[68],"Attention":[69],"dissipation,":[70,131],"where":[71,99],"attention":[73,130,168],"information":[74],"decoder,":[77],"which":[78],"crucial":[80],"representing":[82],"segmentation":[83],"masks,":[84],"collapses":[85],"into":[86],"diffuse":[88],"non-semantic":[90],"form":[91],"under":[92],"low-bit":[93],"quantization;":[94],"(2)":[96],"Reconstruction":[97,161],"oscillation,":[98],"bidirectional":[100],"coupling":[101],"within":[102],"two-way":[104],"transformer":[105],"introduces":[106],"cross-branch":[107],"error":[108],"interference":[109],"destabilizes":[111],"convergence.":[112,176],"To":[113],"tackle":[114],"these":[115],"issues,":[116],"we":[117,132,156],"propose":[118],"CAR-SAM,":[119],"unified":[121],"quantization":[122,141],"framework":[123],"tailored":[124],"SAMs.":[126],"Firstly,":[127],"mitigate":[129,151],"introduce":[133],"MatMul-Aware":[134],"Compensation":[135],"(MAC)":[136],"mechanism":[137],"that":[138,164,180],"transfers":[139],"activation-induced":[140],"errors":[142],"MatMul":[144],"preceding":[146],"linear":[147],"weights.":[148],"Secondly,":[149],"oscillation":[152],"decoder":[154],"optimization,":[155],"develop":[157],"Joint":[159],"Cross-Attention":[160],"(JCAR)":[162],"strategy":[163],"jointly":[165],"reconstructs":[166],"coupled":[167],"branches,":[169],"suppressing":[170],"oscillatory":[171],"behavior":[172],"promoting":[174],"stable":[175],"Extensive":[177],"experiments":[178],"show":[179],"CAR-SAM":[181],"robustly":[182],"quantizes":[183],"models":[185],"down":[186],"4-bit":[188],"precision,":[189],"surpassing":[190],"14.6%":[194],"6.6%":[196],"mAP":[197],"SAM-B":[199],"SAM-L":[201],"respectively.":[202]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
