{"id":"https://openalex.org/W2978700917","doi":"https://doi.org/10.1109/ijcnn.2019.8852102","title":"Batch Mode Active Learning with Nonlocal Self-Similarity Prior for Semantic Segmentation","display_name":"Batch Mode Active Learning with Nonlocal Self-Similarity Prior for Semantic Segmentation","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978700917","doi":"https://doi.org/10.1109/ijcnn.2019.8852102","mag":"2978700917"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5112903417","display_name":"Yao Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yao Tan","raw_affiliation_strings":["School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056686459","display_name":"Qinghua Hu","orcid":"https://orcid.org/0000-0001-7765-8095"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qinghua Hu","raw_affiliation_strings":["School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004929939","display_name":"Zhibin Du","orcid":"https://orcid.org/0000-0001-5795-3580"},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhibin Du","raw_affiliation_strings":["China Automotive Technology & Research Center, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"China Automotive Technology & Research Center, Tianjin, China","institution_ids":["https://openalex.org/I4210094894"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112903417"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57426511,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9984999895095825,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9984999895095825,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9883999824523926,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9882000088691711,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7365317940711975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6374390721321106},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.626785397529602},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.574383556842804},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49810314178466797},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4679390788078308},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.4477776885032654},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44706445932388306},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.42046597599983215},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.27284717559814453}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7365317940711975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6374390721321106},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.626785397529602},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.574383556842804},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49810314178466797},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4679390788078308},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.4477776885032654},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44706445932388306},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.42046597599983215},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27284717559814453},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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":69,"referenced_works":["https://openalex.org/W3464401","https://openalex.org/W66763315","https://openalex.org/W123226248","https://openalex.org/W582134693","https://openalex.org/W601603264","https://openalex.org/W1528361845","https://openalex.org/W1845402413","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1923697677","https://openalex.org/W1973207753","https://openalex.org/W1975672287","https://openalex.org/W1978130285","https://openalex.org/W1994757995","https://openalex.org/W1995875735","https://openalex.org/W2012878613","https://openalex.org/W2014940628","https://openalex.org/W2019329118","https://openalex.org/W2025566624","https://openalex.org/W2037227137","https://openalex.org/W2054279472","https://openalex.org/W2063863981","https://openalex.org/W2123229215","https://openalex.org/W2124244761","https://openalex.org/W2124351162","https://openalex.org/W2124592697","https://openalex.org/W2137795521","https://openalex.org/W2140524605","https://openalex.org/W2150066425","https://openalex.org/W2171671120","https://openalex.org/W2171943915","https://openalex.org/W2207282238","https://openalex.org/W2273666108","https://openalex.org/W2340897893","https://openalex.org/W2341531400","https://openalex.org/W2343514605","https://openalex.org/W2395611524","https://openalex.org/W2471138382","https://openalex.org/W2609077090","https://openalex.org/W2630837129","https://openalex.org/W2735257517","https://openalex.org/W2768367887","https://openalex.org/W2772993714","https://openalex.org/W2774918944","https://openalex.org/W2903158431","https://openalex.org/W2951786554","https://openalex.org/W2962850830","https://openalex.org/W2963881378","https://openalex.org/W2963902936","https://openalex.org/W2964059111","https://openalex.org/W2964309882","https://openalex.org/W3104282073","https://openalex.org/W4285719527","https://openalex.org/W4301329587","https://openalex.org/W6617145748","https://openalex.org/W6618476874","https://openalex.org/W6638701278","https://openalex.org/W6639824700","https://openalex.org/W6640054144","https://openalex.org/W6640295612","https://openalex.org/W6680113041","https://openalex.org/W6694212129","https://openalex.org/W6704826796","https://openalex.org/W6735374517","https://openalex.org/W6739696289","https://openalex.org/W6747231328","https://openalex.org/W6756615331","https://openalex.org/W6766376895","https://openalex.org/W6786451373"],"related_works":["https://openalex.org/W2069133146","https://openalex.org/W1663079876","https://openalex.org/W2017545316","https://openalex.org/W4286681602","https://openalex.org/W3209312100","https://openalex.org/W2114797768","https://openalex.org/W2380654781","https://openalex.org/W2176214140","https://openalex.org/W1522196789","https://openalex.org/W2516873349"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,164,204],"is":[2,16,154,187],"a":[3,53,59,70,94],"task":[4],"that":[5,193],"heavily":[6],"relies":[7],"on":[8,173],"the":[9,25,36,41,82,88,99,108,111,114,118,123,130,142,147,162,174,184,203],"annotated":[10],"data.":[11],"The":[12,151,181,190],"image":[13,89,100],"annotation":[14,37],"cost":[15,38],"very":[17],"expensive.":[18],"While":[19],"active":[20,74,84,199],"learning":[21,75,85,200],"aims":[22],"to":[23,51,128,145],"select":[24,52],"most":[26],"valuable":[27],"samples":[28],"by":[29,156],"an":[30],"iterative":[31],"procedure.":[32],"It":[33,105],"can":[34,106],"reduce":[35],"and":[39,120,176],"improve":[40],"performance":[42],"of":[43,55,58,102,113,125,132,149,183],"classification.":[44],"In":[45,65,137,166],"semantic":[46,78,163],"segmentation,":[47],"its":[48],"more":[49],"common":[50],"batch":[54,72],"instances":[56],"instead":[57],"single":[60],"instance":[61],"at":[62,134],"each":[63,135],"iteration.":[64,136],"this":[66,167],"paper,":[67],"we":[68,91,139,169],"propose":[69],"novel":[71],"mode":[73],"algorithm":[76,195],"for":[77,87],"segmentation.":[79],"Different":[80],"from":[81],"previous":[83],"algorithms":[86],"classification,":[90],"first":[92],"introduce":[93],"new":[95],"selecting":[96],"criterion":[97,124],":":[98],"prior":[101],"nonlocal":[103,126,185],"self-similarity.":[104],"measure":[107,146],"interaction":[109],"between":[110],"pixels":[112],"image.":[115],"We":[116],"combine":[117],"informativeness":[119],"representativeness":[121],"with":[122],"self-similarity":[127,186],"complete":[129],"selection":[131],"images":[133],"addition,":[138],"also":[140,188],"use":[141],"model":[143,152],"uncertainty":[144,153],"information":[148],"samples.":[150],"captured":[155],"using":[157],"Monte":[158],"Carlo":[159],"Dropout":[160],"in":[161],"model.":[165],"work,":[168],"evaluate":[170],"our":[171,194],"method":[172],"CamVid":[175],"PASCAL":[177],"VOC":[178],"2012":[179],"datasets.":[180],"importance":[182],"assessed.":[189],"experiments":[191],"demonstrate":[192],"outperforms":[196],"current":[197],"state-of-the-art":[198],"methods":[201],"over":[202],"performance.":[205]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}