{"id":"https://openalex.org/W7161722010","doi":"https://doi.org/10.48550/arxiv.2605.18063","title":"The MixCount Dataset: Bridging the Data Gap for Open-Vocabulary Object Counting","display_name":"The MixCount Dataset: Bridging the Data Gap for Open-Vocabulary Object Counting","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161722010","doi":"https://doi.org/10.48550/arxiv.2605.18063"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18063","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18063","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.18063","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008484135","display_name":"Corentin Dumery","orcid":"https://orcid.org/0000-0001-5314-7979"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dumery, Corentin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092108058","display_name":"Niki Amini-Naieni","orcid":"https://orcid.org/0009-0007-8301-1010"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amini-Naieni, Niki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136485515","display_name":"Shervin Naini","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naini, Shervin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136462938","display_name":"Pascal Fua","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fua, Pascal","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":0.20990000665187836,"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":0.20990000665187836,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.2061000019311905,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.07400000095367432,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.8069999814033508},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.8033000230789185},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6765999794006348},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6626999974250793},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.5008000135421753},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.47189998626708984},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4392000138759613},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.428600013256073}],"concepts":[{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.8069999814033508},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.8033000230789185},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7810999751091003},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6765999794006348},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6626999974250793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5468000173568726},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.5008000135421753},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4392000138759613},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4189000129699707},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4066999852657318},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26570001244544983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18063","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18063","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.18063","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18063","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"Object":[0],"counting":[1,52,83,92,119,133,202],"is":[2,40],"a":[3,9,77,176,179,198],"foundational":[4],"vision":[5],"task":[6],"with":[7,75],"over":[8],"decade":[10],"of":[11,90,99],"dedicated":[12],"research,":[13],"yet":[14],"state-of-the-art":[15,132],"models":[16,134,148],"still":[17],"fail":[18],"systematically":[19],"in":[20,45,140,201],"the":[21,87,96,124,141],"mixed-object":[22,82,142],"setting":[23],"that":[24,37,111,127,187],"dominates":[25],"real-world":[26,157],"applications":[27],"such":[28,103],"as":[29,174],"industrial":[30],"inspection":[31],"and":[32,48,59,70,79,101,117,165,178,185],"product":[33],"sorting.":[34],"We":[35,72],"show":[36],"this":[38,74],"gap":[39],"strongly":[41],"driven":[42],"by":[43,161,166],"limitations":[44],"existing":[46,65],"training":[47,146,180],"evaluation":[49],"data:":[50],"real":[51],"datasets":[53],"are":[54],"prohibitively":[55],"expensive":[56],"to":[57,85],"annotate":[58],"suffer":[60],"from":[61],"labeling":[62,102,125],"noise,":[63],"while":[64],"synthetic":[66],"alternatives":[67],"lack":[68],"diversity":[69],"realism.":[71],"address":[73,197],"MixCount,":[76],"dataset":[78,181],"benchmark":[80,177],"for":[81,182],"designed":[84],"target":[86],"failure":[88],"modes":[89],"current":[91],"models.":[93,203],"To":[94],"overcome":[95],"high":[97],"cost":[98],"constructing":[100],"data,":[104,195],"we":[105],"develop":[106],"an":[107],"automatic":[108],"generation":[109],"pipeline":[110],"synthesizes":[112],"images,":[113],"fine-grained":[114,183],"textual":[115],"descriptions,":[116],"pixel-perfect":[118],"annotations":[120],"at":[121],"scale,":[122],"eliminating":[123],"ambiguity":[126],"plagues":[128],"prior":[129],"datasets.":[130],"Evaluating":[131],"on":[135,149,156,163,168],"MixCount":[136,173],"exposes":[137],"severe":[138],"degradation":[139],"setting.":[143],"More":[144],"importantly,":[145],"these":[147],"our":[150,188],"synthesized":[151],"data":[152],"yields":[153],"substantial":[154],"gains":[155],"benchmarks,":[158],"reducing":[159],"MAE":[160],"20.14%":[162],"FSC-147":[164],"18.3%":[167],"PairTally.":[169],"These":[170],"results":[171],"establish":[172],"both":[175],"counting,":[184],"demonstrate":[186],"pipeline,":[189],"which":[190],"produces":[191],"effectively":[192],"unlimited":[193],"labeled":[194],"helps":[196],"long-standing":[199],"bottleneck":[200]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}
