{"id":"https://openalex.org/W3042800203","doi":"https://doi.org/10.1109/tbdata.2020.3009983","title":"Weak Supervision Learning for Object Co-Segmentation","display_name":"Weak Supervision Learning for Object Co-Segmentation","publication_year":2020,"publication_date":"2020-07-17","ids":{"openalex":"https://openalex.org/W3042800203","doi":"https://doi.org/10.1109/tbdata.2020.3009983","mag":"3042800203"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2020.3009983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2020.3009983","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-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/A5101599930","display_name":"Aiping Huang","orcid":"https://orcid.org/0000-0002-6354-9039"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aiping Huang","raw_affiliation_strings":["Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6354-9039","affiliations":[{"raw_affiliation_string":"Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057108758","display_name":"Tiesong Zhao","orcid":"https://orcid.org/0000-0002-7497-8883"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiesong Zhao","raw_affiliation_strings":["Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7497-8883","affiliations":[{"raw_affiliation_string":"Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I80947539"],"apc_list":null,"apc_paid":null,"fwci":0.2875,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.55444075,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"8","issue":"4","first_page":"1129","last_page":"1140"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9998999834060669,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9998999834060669,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9990000128746033,"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/computer-science","display_name":"Computer science","score":0.8442723751068115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7229533195495605},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6123431324958801},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6028148531913757},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5934799909591675},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5345339775085449},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.44327232241630554},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.41900473833084106},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.41064274311065674},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4002968966960907},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.22060272097587585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8442723751068115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7229533195495605},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6123431324958801},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6028148531913757},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5934799909591675},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5345339775085449},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.44327232241630554},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41900473833084106},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.41064274311065674},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4002968966960907},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.22060272097587585}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2020.3009983","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2020.3009983","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8299999833106995}],"awards":[{"id":"https://openalex.org/G3027073719","display_name":"\u57fa\u4e8e\u611f\u77e5\u4e0e\u8bb0\u5fc6\u6a21\u578b\u7684\u79fb\u52a8\u89c6\u9891\u901a\u4fe1\u52a8\u6001\u4f53\u9a8c\u8d28\u91cf\u7814\u7a76","funder_award_id":"61671152","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W88469699","https://openalex.org/W1496451467","https://openalex.org/W1498238238","https://openalex.org/W1693210201","https://openalex.org/W1808456820","https://openalex.org/W1898560071","https://openalex.org/W1963920598","https://openalex.org/W1964884769","https://openalex.org/W1968731684","https://openalex.org/W1996140089","https://openalex.org/W2003033789","https://openalex.org/W2003343330","https://openalex.org/W2004926197","https://openalex.org/W2028590536","https://openalex.org/W2062385571","https://openalex.org/W2080142539","https://openalex.org/W2086052791","https://openalex.org/W2096674800","https://openalex.org/W2110370288","https://openalex.org/W2114542651","https://openalex.org/W2118802082","https://openalex.org/W2121927366","https://openalex.org/W2129260071","https://openalex.org/W2141303268","https://openalex.org/W2141729166","https://openalex.org/W2157244733","https://openalex.org/W2229419338","https://openalex.org/W2295160225","https://openalex.org/W2295326880","https://openalex.org/W2295401494","https://openalex.org/W2313077179","https://openalex.org/W2321559277","https://openalex.org/W2325835689","https://openalex.org/W2415053570","https://openalex.org/W2468480815","https://openalex.org/W2536981762","https://openalex.org/W2553107651","https://openalex.org/W2553149432","https://openalex.org/W2586573945","https://openalex.org/W2760930079","https://openalex.org/W2773771410","https://openalex.org/W2775991602","https://openalex.org/W2794191095","https://openalex.org/W2807912089","https://openalex.org/W2885254978","https://openalex.org/W2891177538","https://openalex.org/W2962766617","https://openalex.org/W4212863985","https://openalex.org/W4244030505"],"related_works":["https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W2580650124","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W2085259108","https://openalex.org/W3123087812","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0],"booming":[1],"of":[2,9,36,90,100],"multimedia":[3],"technologies":[4],"has":[5,24],"promoted":[6],"the":[7,21,29,40,69,75,88,95,106,130,134,153,164,172],"diversity":[8],"visual":[10],"big":[11],"data.":[12],"To":[13,86],"learn":[14],"common":[15,78],"features":[16],"across":[17],"heterogeneous":[18],"image":[19,22,37,101,123],"data,":[20],"co-processing":[23,38],"exhibited":[25],"its":[26],"advantages":[27],"over":[28],"separate":[30],"one.":[31],"Recently,":[32],"an":[33,139],"active":[34],"topic":[35],"is":[39],"object":[41,154],"co-segmentation,":[42],"which":[43],"aims":[44],"at":[45],"simultaneously":[46],"extracting":[47],"and":[48,80,124,147],"segmenting":[49],"shared":[50],"objects":[51],"from":[52],"relevant":[53],"images.":[54,128],"In":[55],"this":[56,60],"paper,":[57],"we":[58,111,137],"address":[59],"problem":[61],"with":[62,133,144,174],"a":[63,175],"weak-supervision-based":[64],"probabilistic":[65,114],"model.":[66],"We":[67],"introduce":[68],"weakly":[70],"supervised":[71],"priors":[72],"to":[73,150,171],"alleviate":[74],"confusion":[76],"between":[77,126],"foreground":[79],"background,":[81],"thereby":[82],"facilitating":[83],"performance":[84,170],"improvement.":[85],"ensure":[87],"validity":[89],"potential":[91],"background":[92],"prior":[93],"knowledge,":[94],"nodes":[96],"on":[97,159],"four":[98],"sides":[99],"are":[102],"respectively":[103],"leveraged":[104],"as":[105],"labelled":[107],"queries.":[108],"After":[109],"that,":[110],"develop":[112],"quantitative":[113],"metrics":[115],"for":[116],"precisely":[117],"measuring":[118],"internal":[119],"consistencies":[120,132],"within":[121],"single":[122],"correlations":[125],"multiple":[127],"Combining":[129],"intra-image":[131],"inter-image":[135],"correlations,":[136],"propose":[138],"optimized":[140],"energy":[141],"function":[142],"coupled":[143],"binary":[145],"labeling":[146],"graph":[148],"connectivity":[149],"carry":[151],"out":[152],"co-segmentation.":[155],"Extensively":[156],"experimental":[157],"results":[158],"real-world":[160],"datasets":[161],"demonstrate":[162],"that":[163],"proposed":[165],"method":[166],"achieves":[167],"superior":[168],"co-segmentation":[169],"state-of-the-arts,":[173],"significantly":[176],"reduced":[177],"time":[178],"consumption.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}