{"id":"https://openalex.org/W4389609690","doi":"https://doi.org/10.1145/3626754","title":"SeeSaw: Interactive Ad-hoc Search Over Image Databases","display_name":"SeeSaw: Interactive Ad-hoc Search Over Image Databases","publication_year":2023,"publication_date":"2023-12-08","ids":{"openalex":"https://openalex.org/W4389609690","doi":"https://doi.org/10.1145/3626754"},"language":"en","primary_location":{"id":"doi:10.1145/3626754","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3626754","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3626754","source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3626754","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077168371","display_name":"Oscar Moll","orcid":"https://orcid.org/0000-0002-5888-4318"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oscar Moll","raw_affiliation_strings":["MIT CSAIL, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5888-4318","affiliations":[{"raw_affiliation_string":"MIT CSAIL, Cambridge, MA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028045291","display_name":"Manuel Favela","orcid":"https://orcid.org/0009-0003-7485-6661"},"institutions":[{"id":"https://openalex.org/I4210110987","display_name":"IIT@MIT","ror":"https://ror.org/01wp8zh54","country_code":"US","type":"facility","lineage":["https://openalex.org/I30771326","https://openalex.org/I4210110987"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Manuel Favela","raw_affiliation_strings":["MIT, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0009-0003-7485-6661","affiliations":[{"raw_affiliation_string":"MIT, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210110987"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037742794","display_name":"Samuel Madden","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Samuel Madden","raw_affiliation_strings":["MIT CSAIL, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-7470-3265","affiliations":[{"raw_affiliation_string":"MIT CSAIL, Cambridge, MA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043450560","display_name":"Vijay Gadepally","orcid":"https://orcid.org/0000-0002-4598-2808"},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vijay Gadepally","raw_affiliation_strings":["MIT Lincoln Laboratory, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-4598-2808","affiliations":[{"raw_affiliation_string":"MIT Lincoln Laboratory, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039133265","display_name":"Michael Cafarella","orcid":"https://orcid.org/0000-0001-6122-0590"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Cafarella","raw_affiliation_strings":["MIT CSAIL, Cambridge, MA, USA"],"raw_orcid":"https://orcid.org/0000-0001-6122-0590","affiliations":[{"raw_affiliation_string":"MIT CSAIL, Cambridge, MA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8158,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79059893,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"1","issue":"4","first_page":"1","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9990000128746033,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9973999857902527,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.789135217666626},{"id":"https://openalex.org/keywords/seesaw-molecular-geometry","display_name":"Seesaw molecular geometry","score":0.7834047079086304},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6497668027877808},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5095772743225098},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.49584564566612244},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4520823359489441},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3767912983894348},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3538469672203064}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.789135217666626},{"id":"https://openalex.org/C159762639","wikidata":"https://www.wikidata.org/wiki/Q2273845","display_name":"Seesaw molecular geometry","level":3,"score":0.7834047079086304},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6497668027877808},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5095772743225098},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.49584564566612244},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4520823359489441},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3767912983894348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3538469672203064},{"id":"https://openalex.org/C186453547","wikidata":"https://www.wikidata.org/wiki/Q2126","display_name":"Neutrino","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C185544564","wikidata":"https://www.wikidata.org/wiki/Q81197","display_name":"Nuclear physics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3626754","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3626754","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3626754","source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3626754","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3626754","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3626754","source":{"id":"https://openalex.org/S4387289859","display_name":"Proceedings of the ACM on Management of Data","issn_l":"2836-6573","issn":["2836-6573"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Management of Data","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389609690.pdf","grobid_xml":"https://content.openalex.org/works/W4389609690.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1899648442","https://openalex.org/W1954152232","https://openalex.org/W2000672666","https://openalex.org/W2051434435","https://openalex.org/W2098824882","https://openalex.org/W2100385916","https://openalex.org/W2103504761","https://openalex.org/W2109812093","https://openalex.org/W2110026675","https://openalex.org/W2125556102","https://openalex.org/W2155099190","https://openalex.org/W2167541073","https://openalex.org/W2548122763","https://openalex.org/W2561675875","https://openalex.org/W2768581363","https://openalex.org/W2799108077","https://openalex.org/W2890102334","https://openalex.org/W2979509742","https://openalex.org/W3040475362","https://openalex.org/W4210997624","https://openalex.org/W4213009331","https://openalex.org/W4236362309"],"related_works":["https://openalex.org/W2497468103","https://openalex.org/W3147366289","https://openalex.org/W160677519","https://openalex.org/W3004732674","https://openalex.org/W2026558218","https://openalex.org/W1999832398","https://openalex.org/W3154683939","https://openalex.org/W2164797156","https://openalex.org/W4384261762","https://openalex.org/W1995114695"],"abstract_inverted_index":{"As":[0],"image":[1,11,41,97],"datasets":[2,26,42,55,98,210],"become":[3],"ubiquitous,":[4],"the":[5,109,127],"problem":[6],"of":[7,79,111,120,130,228,237,246],"ad-hoc":[8,35,94],"searches":[9,66,95],"over":[10],"data":[12,18,124],"is":[13,75,89,138],"increasingly":[14],"important.":[15],"Many":[16],"high-level":[17],"tasks":[19,224],"in":[20,86,108,122,126,140,177],"machine":[21],"learning,":[22],"such":[23,56],"as":[24,43,57],"constructing":[25],"for":[27,92,136,206],"training":[28],"and":[29,179,193,199,211,239],"testing":[30],"object":[31],"detectors,":[32],"imply":[33],"finding":[34],"objects":[36],"or":[37],"scenes":[38],"within":[39],"large":[40],"a":[44,76,90,195,214,231,235,241,244],"key":[45,134],"sub-problem.":[46],"New":[47],"foundational":[48],"visual-semantic":[49],"embeddings":[50,102],"trained":[51],"on":[52,67,96,222,230,243],"massive":[53],"web":[54],"Contrastive":[58],"Language-Image":[59],"Pre-Training":[60],"(CLIP)":[61],"can":[62,155],"help":[63,115],"users":[64,116,207],"start":[65],"their":[68,123],"own":[69],"data,":[70],"but":[71],"we":[72],"find":[73,200],"there":[74],"long":[77,128],"tail":[78,129],"queries":[80,249],"where":[81,250],"these":[82],"models":[83],"fall":[84],"short":[85],"practice.":[87],"Seesaw":[88,137,170,201,217],"system":[91],"interactive":[93],"that":[99,174],"integrates":[100],"state-of-the-art":[101,152,196],"like":[103],"CLIP":[104,191,251],"with":[105],"user":[106],"feedback":[107,147],"form":[110],"box":[112],"annotations":[113],"to":[114,145,159,165,188,194],"quickly":[117],"locate":[118],"images":[119],"interest":[121],"even":[125],"harder":[131],"queries.":[132,216],"One":[133],"challenge":[135],"that,":[139],"practice,":[141],"many":[142],"sensible":[143],"approaches":[144],"incorporating":[146],"into":[148],"future":[149],"results,":[150],"including":[151],"active-learning":[153,197],"algorithms,":[154],"worsen":[156],"results":[157,205],"compared":[158],"introducing":[160],"no":[161],"feedback,":[162],"partly":[163],"due":[164],"CLIP's":[166],"high-average":[167],"performance.":[168],"Therefore,":[169],"includes":[171],"several":[172],"algorithms":[173],"empirically":[175],"result":[176],"larger":[178],"also":[180],"more":[181,212,247],"consistent":[182],"improvements.":[183],"We":[184],"compare":[185],"Seesaw's":[186],"accuracy":[187],"both":[189],"using":[190],"alone":[192,252],"baseline":[198],"consistently":[202],"helps":[203],"improve":[204],"across":[208],"four":[209],"than":[213],"thousand":[215],"increases":[218],"Average":[219],"Precision":[220],"(AP)":[221],"search":[223],"by":[225,240],"an":[226],"average":[227],".08":[229],"wide":[232],"benchmark":[233],"(from":[234],"base":[236],".72),":[238],".27":[242],"subset":[245],"difficult":[248],"performs":[253],"poorly.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
