{"id":"https://openalex.org/W2153430446","doi":"https://doi.org/10.1109/cvpr.2003.1211385","title":"An efficient approach to learning inhomogeneous Gibbs model","display_name":"An efficient approach to learning inhomogeneous Gibbs model","publication_year":2003,"publication_date":"2003-11-04","ids":{"openalex":"https://openalex.org/W2153430446","doi":"https://doi.org/10.1109/cvpr.2003.1211385","mag":"2153430446"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2003.1211385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2003.1211385","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.","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/A5100628622","display_name":"Ziqiang Liu","orcid":"https://orcid.org/0009-0006-8823-4734"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ziqiang Liu","raw_affiliation_strings":["Visiting from Vision Lab, Department of Inf. Science and Electronic Engineering, University of Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"Visiting from Vision Lab, Department of Inf. Science and Electronic Engineering, University of Zhejiang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100420428","display_name":"Hong Chen","orcid":"https://orcid.org/0000-0002-3764-1632"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Chen","raw_affiliation_strings":["Microsoft Research Asia, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061000201","display_name":"Heung\u2010Yeung Shum","orcid":"https://orcid.org/0000-0002-4684-911X"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heung-Yeung Shum","raw_affiliation_strings":["Microsoft Research Asia, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100628622"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9364,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.82420888,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"24","issue":null,"first_page":"I","last_page":"425"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9962999820709229,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9962999820709229,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9959999918937683,"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/T10057","display_name":"Face and Expression Recognition","score":0.9797999858856201,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.7733252048492432},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.7419534921646118},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.621399998664856},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5543399453163147},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.529090166091919},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4975762665271759},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4608684480190277},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45759421586990356},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.4417412281036377},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.43677523732185364},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.42681941390037537},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.421101450920105},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.41781026124954224},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.41741943359375},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.41305258870124817},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3834715187549591},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3818318843841553},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34198862314224243},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2695568799972534},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18894168734550476},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08789557218551636},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.0778786838054657}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7733252048492432},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.7419534921646118},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.621399998664856},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5543399453163147},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.529090166091919},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4975762665271759},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4608684480190277},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45759421586990356},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.4417412281036377},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.43677523732185364},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.42681941390037537},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.421101450920105},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.41781026124954224},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.41741943359375},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.41305258870124817},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3834715187549591},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3818318843841553},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34198862314224243},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2695568799972534},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18894168734550476},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08789557218551636},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0778786838054657},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2003.1211385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2003.1211385","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.114.2305","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.2305","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~misc-read/talks-2003/gibbs-model.pdf","raw_type":"text"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-162299","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-162299","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2001792610","https://openalex.org/W2096175520","https://openalex.org/W2124865782","https://openalex.org/W2126174118","https://openalex.org/W2127955319","https://openalex.org/W2153381665","https://openalex.org/W2160842254","https://openalex.org/W2172803778","https://openalex.org/W6674650171","https://openalex.org/W6678685480"],"related_works":["https://openalex.org/W2133435412","https://openalex.org/W2950438842","https://openalex.org/W2167163570","https://openalex.org/W4300707824","https://openalex.org/W3122506910","https://openalex.org/W1980187201","https://openalex.org/W3121862329","https://openalex.org/W3213105899","https://openalex.org/W2164777412","https://openalex.org/W1980147245"],"abstract_inverted_index":{"The":[0],"inhomogeneous":[1],"Gibbs":[2],"model":[3,14],"(IGM)":[4],"(Liu":[5],"et":[6],"al.,":[7],"2001)":[8],"is":[9,24,65,72,98],"an":[10,41],"effective":[11],"maximum":[12],"entropy":[13],"in":[15],"characterizing":[16],"complex":[17],"high-dimensional":[18,119,148],"distributions.":[19],"However,":[20],"its":[21],"training":[22],"process":[23],"so":[25],"slow":[26],"that":[27],"the":[28,58,102,112,134,140],"applicability":[29],"of":[30,47,127,130,142,151],"IGM":[31,50],"has":[32],"been":[33],"greatly":[34],"restricted.":[35],"In":[36,49,80],"this":[37],"paper,":[38],"we":[39],"propose":[40],"approach":[42,83,122,144],"for":[43,77],"fast":[44],"parameter":[45,78,113],"learning":[46,146],"IGM.":[48,136],"learning,":[51],"features":[52],"are":[53],"incrementally":[54],"constructed":[55],"to":[56,74,101,110,133],"constrain":[57],"learnt":[59],"distribution.":[60],"When":[61],"a":[62,85,125,147],"new":[63],"feature":[64],"added,":[66],"Markov-chain":[67],"Monte":[68],"Carlo":[69],"(MCMC)":[70],"sampling":[71,106],"repeated":[73],"draw":[75],"samples":[76],"learning.":[79],"contrast,":[81],"our":[82,95,121,143],"constructs":[84],"closed-form":[86],"reference":[87,96],"distribution":[88,97,150],"using":[89],"approximate":[90],"information":[91],"gain":[92],"criteria.":[93],"Because":[94],"very":[99],"close":[100],"optimal":[103],"one,":[104],"importance":[105],"can":[107],"be":[108],"used":[109],"accelerate":[111],"optimization":[114],"process.":[115],"For":[116],"problems":[117],"with":[118],"distributions,":[120],"typically":[123],"achieves":[124],"speedup":[126],"two":[128],"orders":[129],"magnitude":[131],"compared":[132],"original":[135],"We":[137],"further":[138],"demonstrate":[139],"efficiency":[141],"by":[145],"joint":[149],"face":[152],"images":[153],"and":[154],"their":[155],"corresponding":[156],"caricatures.":[157]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}