{"id":"https://openalex.org/W3196827899","doi":"https://doi.org/10.1145/3474085.3475175","title":"Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal","display_name":"Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W3196827899","doi":"https://doi.org/10.1145/3474085.3475175","mag":"3196827899"},"language":"en","primary_location":{"id":"doi:10.1145/3474085.3475175","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3474085.3475175","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3474085.3475175","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3474085.3475175","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lei Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Lei Zhu","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhaojing Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhaojing Luo","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Meihui Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meihui Zhang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Gang Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Chen","raw_affiliation_strings":["Zhejiang University, Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kaiping Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Kaiping Zheng","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12173992,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3024","last_page":"3033"},"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":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9753000140190125,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9697999954223633,"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/embedding","display_name":"Embedding","score":0.629800021648407},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5011000037193298},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4787999987602234},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4641000032424927},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4496999979019165},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4481000006198883},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4325999915599823},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.36660000681877136},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.34850001335144043}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7760000228881836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.699999988079071},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.629800021648407},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5011000037193298},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4787999987602234},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4641000032424927},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4481000006198883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43950000405311584},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.33410000801086426},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2856000065803528},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28049999475479126},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2632000148296356},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.26089999079704285},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2590999901294708}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3474085.3475175","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3474085.3475175","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3474085.3475175","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2109.04284","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.04284","pdf_url":"https://arxiv.org/pdf/2109.04284","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"}],"best_oa_location":{"id":"doi:10.1145/3474085.3475175","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3474085.3475175","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3474085.3475175","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6748694274","display_name":null,"funder_award_id":"62050099","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"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3196827899.pdf","grobid_xml":"https://content.openalex.org/works/W3196827899.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1202352811","https://openalex.org/W2049633694","https://openalex.org/W2062022900","https://openalex.org/W2108598243","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2251084241","https://openalex.org/W2287418003","https://openalex.org/W2530144925","https://openalex.org/W2593768305","https://openalex.org/W2627183927","https://openalex.org/W2897195437","https://openalex.org/W2897556776","https://openalex.org/W2904549000","https://openalex.org/W2933775054","https://openalex.org/W2962687275","https://openalex.org/W2963163009","https://openalex.org/W2963240485","https://openalex.org/W2963314614","https://openalex.org/W2963735582","https://openalex.org/W2964288524","https://openalex.org/W2969893028","https://openalex.org/W2984810221","https://openalex.org/W2991405316","https://openalex.org/W3108560336","https://openalex.org/W3175294391"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1,21,67],"has":[2],"made":[3],"a":[4,25,48,54,163,213,225,235,243,278],"tremendous":[5],"impact":[6],"on":[7,109,250,302],"various":[8],"applications":[9],"in":[10,128,143,154,188,203],"multimedia,":[11],"such":[12],"as":[13,234],"media":[14],"interpretation":[15],"and":[16,115,180,207,241,280,306,311],"multimodal":[17],"retrieval.":[18],"However,":[19,69],"deep":[20,66],"models":[22],"usually":[23],"require":[24],"large":[26],"amount":[27],"of":[28,43,136,141,152,199,299],"labeled":[29],"data":[30,103,114,201,261],"to":[31,53,194,217,283,289,295],"achieve":[32],"satisfactory":[33],"performance.":[34],"In":[35,158],"multimedia":[36],"analysis,":[37],"domain":[38,52,74,85,95,146],"adaptation":[39,75,96,247],"studies":[40,93],"the":[41,62,94,99,117,124,129,134,144,155,177,189,196,200,204,209,229,251,263,270,285,297],"problem":[42,97,135],"cross-domain":[44,78],"knowledge":[45],"transfer":[46],"from":[47,287,309],"label":[49,55],"rich":[50],"source":[51,84,102,113,156,220],"scarce":[56],"target":[57,145,260],"domain,":[58],"thus":[59],"potentially":[60],"alleviates":[61],"annotation":[63],"requirement":[64],"for":[65,77,172,237,268],"models.":[68],"we":[70,161,175,276],"find":[71],"that":[72,317],"contemporary":[73],"methods":[76,108],"image":[79],"understanding":[80],"perform":[81],"poorly":[82],"when":[83],"is":[86],"noisy.":[87,106],"Weakly":[88],"Supervised":[89],"Domain":[90,169],"Adaptation":[91,170],"(WSDA)":[92],"under":[98],"scenario":[100],"where":[101],"can":[104],"be":[105,148],"Prior":[107],"WSDA":[110,323],"remove":[111],"noisy":[112,219,265],"align":[116],"marginal":[118],"distribution":[119],"across":[120,273],"domains":[121],"without":[122],"considering":[123],"fine-grained":[125],"semantic":[126,271],"structure":[127,272],"embedding":[130,190,205],"space,":[131],"which":[132,227,257],"have":[133],"class":[137,185,266],"misalignment,":[138],"e.g.,":[139],"features":[140,151],"cats":[142],"might":[147],"mapped":[149],"near":[150],"dogs":[153],"domain.":[157],"this":[159],"paper,":[160],"propose":[162,193,242],"novel":[164,244],"method,":[165],"termed":[166],"Noise":[167],"Tolerant":[168],"(NTDA),":[171],"WSDA.":[173],"Specifically,":[174],"adopt":[176],"cluster":[178,182],"assumption":[179],"learn":[181],"discriminatively":[183],"with":[184,212,262],"prototypes":[186,267],"(centroids)":[187],"space.":[191],"We":[192,222,291],"leverage":[195],"location":[197,210],"information":[198,211],"points":[202],"space":[206],"model":[208,216,233],"Gaussian":[214,230],"mixture":[215,231],"identify":[218],"data.":[221],"then":[223],"design":[224],"network":[226,286],"incorporates":[228],"noise":[232,239],"sub-module":[236],"unsupervised":[238],"removal":[240],"cluster-level":[245],"adversarial":[246],"method":[248,301,319],"based":[249],"Generative":[252],"Adversarial":[253],"Network":[254],"(GAN)":[255],"framework":[256],"aligns":[258],"unlabeled":[259],"less":[264],"mapping":[269],"domains.":[274],"Finally,":[275],"devise":[277],"simple":[279],"effective":[281],"algorithm":[282],"train":[284],"end":[288],"end.":[290],"conduct":[292],"extensive":[293],"experiments":[294],"evaluate":[296],"effectiveness":[298],"our":[300,318],"both":[303],"general":[304],"images":[305,308],"medical":[307],"COVID-19":[310],"e-commerce":[312],"datasets.":[313],"The":[314],"results":[315],"show":[316],"significantly":[320],"outperforms":[321],"state-of-the-art":[322],"methods.":[324]},"counts_by_year":[],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2021-09-13T00:00:00"}
