{"id":"https://openalex.org/W4403792118","doi":"https://doi.org/10.1145/3664647.3680976","title":"Boosting Semi-supervised Crowd Counting with Scale-based Active Learning","display_name":"Boosting Semi-supervised Crowd Counting with Scale-based Active Learning","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403792118","doi":"https://doi.org/10.1145/3664647.3680976"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3680976","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-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/A5113431536","display_name":"Shiwei Zhang","orcid":"https://orcid.org/0000-0003-2870-3974"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiwei Zhang","raw_affiliation_strings":["School of Software Engineering, Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-2870-3974","affiliations":[{"raw_affiliation_string":"School of Software Engineering, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101867597","display_name":"Wei Ke","orcid":"https://orcid.org/0000-0002-2899-0371"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Ke","raw_affiliation_strings":["School of Software Engineering, Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-2899-0371","affiliations":[{"raw_affiliation_string":"School of Software Engineering, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004735889","display_name":"Shuai Liu","orcid":"https://orcid.org/0000-0002-0327-6729"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Liu","raw_affiliation_strings":["School of Software Engineering, Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-0327-6729","affiliations":[{"raw_affiliation_string":"School of Software Engineering, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026880795","display_name":"Xiaopeng Hong","orcid":"https://orcid.org/0000-0002-0611-0636"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaopeng Hong","raw_affiliation_strings":["Harbin Institute of Technology, Harbin, China"],"raw_orcid":"https://orcid.org/0000-0002-0611-0636","affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100378750","display_name":"Tong Zhang","orcid":"https://orcid.org/0000-0001-5818-4285"},"institutions":[{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Tong Zhang","raw_affiliation_strings":["Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland"],"raw_orcid":"https://orcid.org/0000-0001-5818-4285","affiliations":[{"raw_affiliation_string":"Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland","institution_ids":["https://openalex.org/I5124864"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5273,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.85884711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"8681","last_page":"8690"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9958999752998352,"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/T11220","display_name":"Water Systems and Optimization","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/boosting","display_name":"Boosting (machine learning)","score":0.7486833333969116},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7044169306755066},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5680484771728516},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5163072943687439},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.5156902074813843},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.4918837547302246},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4755525290966034}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7486833333969116},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7044169306755066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5680484771728516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5163072943687439},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.5156902074813843},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.4918837547302246},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4755525290966034},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3680976","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1553262910","https://openalex.org/W1978633512","https://openalex.org/W2463631526","https://openalex.org/W2618530766","https://openalex.org/W2745597836","https://openalex.org/W2798820905","https://openalex.org/W2813911573","https://openalex.org/W2969620138","https://openalex.org/W2986514296","https://openalex.org/W3015469128","https://openalex.org/W3027606690","https://openalex.org/W3048083751","https://openalex.org/W3097407159","https://openalex.org/W3109242411","https://openalex.org/W3126263946","https://openalex.org/W3154723007","https://openalex.org/W3184557372","https://openalex.org/W3186674029","https://openalex.org/W3203845557","https://openalex.org/W4214637271","https://openalex.org/W4214665794","https://openalex.org/W4214828374","https://openalex.org/W4225264236","https://openalex.org/W4246999471","https://openalex.org/W4287816982","https://openalex.org/W4298326481","https://openalex.org/W4304098313","https://openalex.org/W4307097660","https://openalex.org/W4312307527","https://openalex.org/W4312613051","https://openalex.org/W4313182800","https://openalex.org/W4313203673","https://openalex.org/W4386065414","https://openalex.org/W4386076535","https://openalex.org/W4386699346","https://openalex.org/W4388430863","https://openalex.org/W4390873023","https://openalex.org/W4390873978"],"related_works":["https://openalex.org/W3082059448","https://openalex.org/W4313640622","https://openalex.org/W2056314584","https://openalex.org/W4206195464","https://openalex.org/W1492505081","https://openalex.org/W132838958","https://openalex.org/W2513638114","https://openalex.org/W4390062853","https://openalex.org/W2340127552","https://openalex.org/W4389256085"],"abstract_inverted_index":{"The":[0,64],"core":[1],"of":[2,29,72,76,102,111],"active":[3,20,46,169],"semi-supervised":[4,129,167,170],"crowd":[5,35,130,171],"counting":[6,131,147,172],"is":[7,67,93],"the":[8,13,24,27,34,57,70,73,96,99,109,112,117,128,135,146,156],"sample":[9],"selection":[10],"criteria.":[11],"However,":[12],"scale":[14,28],"factor":[15],"has":[16,159],"been":[17],"neglected":[18],"in":[19,33,133],"learning":[21],"approaches":[22],"despite":[23],"fact":[25],"that":[26],"heads":[30],"varies":[31],"drastically":[32],"images.":[36],"In":[37],"this":[38,87],"paper,":[39],"we":[40,121],"propose":[41],"a":[42,123],"simple":[43],"yet":[44],"effective":[45],"labeling":[47],"strategy":[48],"to":[49,164],"explicitly":[50],"select":[51],"informative":[52,114],"unlabeled":[53,138],"images,":[54,106],"guided":[55],"by":[56,95],"intra-scale":[58,65],"uncertainty":[59,66,88],"and":[60,104,168],"inter-scale":[61],"inconsistency":[62,92],"metrics.":[63],"quantified":[68],"through":[69],"sum":[71],"query-level":[74,100],"entropy":[75],"images":[77,115,139],"at":[78],"different":[79],"scales.":[80],"Images":[81],"are":[82,140],"initially":[83],"ranked":[84],"based":[85],"on":[86,152],"for":[89,108,127,137],"preselection.":[90],"Inter-scale":[91],"measured":[94],"divergence":[97],"between":[98],"predictions":[101],"upscaled":[103],"downscaled":[105],"allowing":[107],"identification":[110],"most":[113],"exhibiting":[116],"highest":[118],"inconsistency.":[119],"Additionally,":[120],"implement":[122],"progressive":[124],"updating":[125],"scheme":[126],"framework,":[132],"which":[134],"pseudo-labels":[136],"refined":[141],"iteratively.":[142],"It":[143],"further":[144],"improves":[145],"accuracy.":[148],"Through":[149],"extensive":[150],"experiments":[151],"widely":[153],"used":[154],"benchmarks,":[155],"proposed":[157],"approach":[158],"demonstrated":[160],"superior":[161],"performance":[162],"compared":[163],"previous":[165],"state-of-the-art":[166],"methods.":[173]},"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"}
