{"id":"https://openalex.org/W3000066289","doi":"https://doi.org/10.1145/3338533.3366555","title":"Excluding the Misleading Relatedness Between Attributes in Multi-Task Attribute Recognition Network","display_name":"Excluding the Misleading Relatedness Between Attributes in Multi-Task Attribute Recognition Network","publication_year":2019,"publication_date":"2019-12-15","ids":{"openalex":"https://openalex.org/W3000066289","doi":"https://doi.org/10.1145/3338533.3366555","mag":"3000066289"},"language":"en","primary_location":{"id":"doi:10.1145/3338533.3366555","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3338533.3366555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Multimedia Asia","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/A5076066715","display_name":"Sirui Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sirui Cai","raw_affiliation_strings":["School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007282525","display_name":"Yuchun Fang","orcid":"https://orcid.org/0000-0002-7085-8876"},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchun Fang","raw_affiliation_strings":["School of Computer Engineering and Science, Shanghai University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering and Science, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076066715"],"corresponding_institution_ids":["https://openalex.org/I141962983"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59696834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.998199999332428,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9980000257492065,"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":0.7843258380889893},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7803957462310791},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6762188673019409},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.657069206237793},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6206413507461548},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.43938153982162476},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4270203113555908},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.41037824749946594},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3426505923271179},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.05265036225318909}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7843258380889893},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7803957462310791},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6762188673019409},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.657069206237793},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6206413507461548},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.43938153982162476},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4270203113555908},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.41037824749946594},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3426505923271179},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.05265036225318909},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3338533.3366555","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3338533.3366555","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Multimedia Asia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W92621565","https://openalex.org/W1896424170","https://openalex.org/W2040171755","https://openalex.org/W2058102599","https://openalex.org/W2102497689","https://openalex.org/W2111025459","https://openalex.org/W2126448884","https://openalex.org/W2163605009","https://openalex.org/W2361187101","https://openalex.org/W2510867321","https://openalex.org/W2536626143","https://openalex.org/W2766111649","https://openalex.org/W2804663377","https://openalex.org/W2949667497","https://openalex.org/W2962786991","https://openalex.org/W2963450096","https://openalex.org/W2963696494","https://openalex.org/W2963721945","https://openalex.org/W2963821905","https://openalex.org/W2963826681","https://openalex.org/W2998115938","https://openalex.org/W3102668440"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"In":[0,57],"the":[1,10,24,91,95,106,110,121,125,131,137],"attribute":[2],"recognition":[3],"area,":[4],"attributes":[5,83],"that":[6,120],"are":[7],"unrelated":[8,82,111],"in":[9,19,55,136],"real":[11],"world":[12],"may":[13],"have":[14],"a":[15,20,29,36],"high":[16],"co-occurrence":[17],"rate":[18],"dataset":[21,25,43],"due":[22],"to":[23,68,78,98],"bias,":[26],"which":[27],"forms":[28],"misleading":[30,107],"relatedness.":[31],"A":[32],"neural":[33,38,128,134],"network,":[34,39],"especially":[35],"multi-task":[37,96,133],"trained":[40],"on":[41,115],"this":[42,46,58,70],"would":[44],"learn":[45],"relatedness,":[47],"and":[48,102,130],"be":[49,99],"misled":[50],"when":[51],"it":[52],"is":[53],"used":[54],"practice.":[56],"paper,":[59],"we":[60],"propose":[61],"Share-and-Compete":[62],"Multi-Task":[63],"deep":[64],"learning":[65],"(SCMTL)":[66],"model":[67,73,97,123],"handle":[69],"problem.":[71],"This":[72],"uses":[74],"adversarial":[75],"training":[76],"methods":[77],"enhance":[79],"competition":[80],"between":[81,87,109],"while":[84],"keeping":[85],"sharing":[86],"related":[88],"attributes,":[89],"making":[90],"task-specific":[92],"layer":[93],"of":[94],"more":[100],"specific":[101],"thus":[103],"rule":[104],"out":[105],"relatedness":[108],"attributes.":[112],"Experiments":[113],"performed":[114],"elaborately":[116],"designed":[117],"datasets":[118],"show":[119],"proposed":[122],"outperforms":[124],"single":[126],"task":[127],"network":[129,135],"traditional":[132],"situation":[138],"mentioned":[139],"above.":[140]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}