{"id":"https://openalex.org/W7162105558","doi":"https://doi.org/10.48550/arxiv.2605.22777","title":"DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders","display_name":"DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162105558","doi":"https://doi.org/10.48550/arxiv.2605.22777"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.22777","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22777","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.2605.22777","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136744820","display_name":"Tianhang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tianhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136772245","display_name":"Yitong Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136810507","display_name":"Wei Song","orcid":"https://orcid.org/0000-0001-6696-1208"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136786226","display_name":"Zuxuan Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zuxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136756719","display_name":"Li, Min, 1977 Apr. 2-","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136784909","display_name":"Jiaqi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiaqi","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9703999757766724,"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":0.9703999757766724,"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":0.010200000368058681,"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/T11094","display_name":"Face Recognition and Perception","score":0.0035000001080334187,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7616999745368958},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6575000286102295},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5776000022888184},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5210000276565552},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4700999855995178},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4474000036716461}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8226000070571899},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7616999745368958},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6575000286102295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.598800003528595},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5776000022888184},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5210000276565552},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4700999855995178},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4474000036716461},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38609999418258667},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3086000084877014},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2696000039577484},{"id":"https://openalex.org/C2776674983","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image editing","level":3,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.22777","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22777","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.2605.22777","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22777","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Representation":[0],"Autoencoders":[1],"(RAEs)":[2],"leverage":[3],"frozen":[4,151],"vision":[5],"foundation":[6],"models":[7],"(VFMs)":[8],"as":[9],"tokenizer":[10],"encoders,":[11],"providing":[12],"robust":[13],"high-level":[14],"representations":[15],"that":[16,79],"facilitate":[17],"fast":[18],"convergence":[19,171],"and":[20,40,55,100,116,129,142,162,181],"high-quality":[21],"generation":[22,39],"in":[23,43],"latent":[24],"diffusion":[25],"models.":[26],"However,":[27],"freezing":[28],"the":[29,51,95,122,150],"VFM":[30,85],"inherently":[31],"constrains":[32],"its":[33],"spatial":[34],"reconstruction":[35,99,127,148],"capacity,":[36],"limiting":[37],"fine-grained":[38,81],"image":[41],"editing;":[42],"contrast,":[44],"incorporating":[45],"reconstruction-oriented":[46],"signals":[47],"via":[48],"fine-tuning":[49],"disrupts":[50],"pretrained":[52],"semantic":[53],"space":[54],"degrades":[56],"generative":[57,108,130,165],"fidelity.":[58],"To":[59],"address":[60],"this":[61],"trade-off,":[62,124],"we":[63],"propose":[64],"DecQ,":[65],"a":[66],"simple":[67],"yet":[68],"effective":[69],"framework":[70],"for":[71,164],"RAEs.":[72],"Specifically,":[73],"DecQ":[74,119,146,167],"introduces":[75],"lightweight":[76],"detail-condensing":[77],"queries":[78,91,141],"extract":[80],"information":[82,112],"from":[83,113,156],"intermediate":[84],"features":[86],"through":[87],"condenser":[88],"modules.":[89],"These":[90],"are":[92,101],"incorporated":[93],"into":[94],"decoder":[96],"to":[97,159],"support":[98],"jointly":[102],"generated":[103],"with":[104,137,183],"patch":[105],"tokens":[106],"during":[107],"modeling.":[109],"By":[110],"aggregating":[111],"both":[114,126],"shallow":[115],"deep":[117],"layers,":[118],"effectively":[120],"mitigates":[121],"reconstruction--generation":[123],"improving":[125],"quality":[128],"performance.":[131],"Our":[132],"experiments":[133],"demonstrate":[134],"that:":[135],"(1)":[136],"only":[138],"8":[139],"additional":[140],"3.9%":[143],"extra":[144],"computation,":[145],"improves":[147],"over":[149],"DINOv2-based":[152],"RAE,":[153,173],"increasing":[154],"PSNR":[155],"19.13":[157],"dB":[158],"22.76":[160],"dB;":[161],"(2)":[163],"modeling,":[166],"achieves":[168],"3.3$\\times$":[169],"faster":[170],"than":[172],"attaining":[174],"an":[175],"FID":[176],"of":[177],"1.41":[178],"without":[179],"guidance":[180],"1.05":[182],"guidance.":[184]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-23T00:00:00"}