{"id":"https://openalex.org/W4388185645","doi":"https://doi.org/10.1145/3581783.3612071","title":"Noise-Robust Continual Test-Time Domain Adaptation","display_name":"Noise-Robust Continual Test-Time Domain Adaptation","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4388185645","doi":"https://doi.org/10.1145/3581783.3612071"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612071","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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/A5031708377","display_name":"Zhiqi Yu","orcid":"https://orcid.org/0000-0001-8631-1915"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiqi Yu","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100338386","display_name":"Jingjing Li","orcid":"https://orcid.org/0000-0002-5504-2529"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjing Li","raw_affiliation_strings":["University of Electronic Science and Technology of China &amp; UESTC in Guangdong, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China &amp; UESTC in Guangdong, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003786587","display_name":"Zhekai Du","orcid":"https://orcid.org/0000-0002-9406-3920"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhekai Du","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017032294","display_name":"Fengling Li","orcid":"https://orcid.org/0009-0000-7185-5784"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Fengling Li","raw_affiliation_strings":["University of Technology Sydney, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"University of Technology Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108048954","display_name":"Lei Zhu","orcid":"https://orcid.org/0000-0002-2993-7142"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhu","raw_affiliation_strings":["Shandong Normal University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"Shandong Normal University, Jinan, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100397616","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0002-5070-4511"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5031708377"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.8741,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79270473,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2654","last_page":"2662"},"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":0.9926000237464905,"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":0.9926000237464905,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.983299970626831,"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/T12676","display_name":"Machine Learning and ELM","score":0.9681000113487244,"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/overfitting","display_name":"Overfitting","score":0.7976901531219482},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7723931670188904},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.7221398949623108},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6563236117362976},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5954939126968384},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5504900813102722},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.518316388130188},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.47490108013153076},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4650230407714844},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4356034994125366},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.4108377695083618},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3261985182762146},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.24719330668449402},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.12915170192718506},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07639479637145996}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7976901531219482},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7723931670188904},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.7221398949623108},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6563236117362976},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5954939126968384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5504900813102722},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.518316388130188},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.47490108013153076},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4650230407714844},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4356034994125366},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.4108377695083618},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3261985182762146},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.24719330668449402},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.12915170192718506},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07639479637145996},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3612071","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3115174496","display_name":null,"funder_award_id":"62176042","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":30,"referenced_works":["https://openalex.org/W1964073652","https://openalex.org/W2053667957","https://openalex.org/W2060024669","https://openalex.org/W2078537554","https://openalex.org/W2093351294","https://openalex.org/W2549139847","https://openalex.org/W2593768305","https://openalex.org/W2618530766","https://openalex.org/W2765440071","https://openalex.org/W2895281799","https://openalex.org/W2954563178","https://openalex.org/W2963697299","https://openalex.org/W2964189064","https://openalex.org/W2981624307","https://openalex.org/W2981952612","https://openalex.org/W2982120826","https://openalex.org/W2998239226","https://openalex.org/W3018638193","https://openalex.org/W3030364939","https://openalex.org/W3034218934","https://openalex.org/W3160470303","https://openalex.org/W3185386766","https://openalex.org/W3198731329","https://openalex.org/W3202345803","https://openalex.org/W4214715922","https://openalex.org/W4312365002","https://openalex.org/W4312897837","https://openalex.org/W4313316091","https://openalex.org/W4319302771","https://openalex.org/W6801615203"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W2490526372","https://openalex.org/W4387800741","https://openalex.org/W2994927414"],"abstract_inverted_index":{"Continual":[0],"test-time":[1],"domain":[2,14],"adaptation":[3],"(TTA)":[4],"is":[5],"a":[6,26,42,118],"challenging":[7],"topic":[8],"in":[9,45,58,147],"the":[10,55,70,75,81,91,96,104,126,148],"field":[11],"of":[12],"source-free":[13],"adaptation,":[15],"which":[16],"focuses":[17],"on":[18,100,108,139],"addressing":[19],"cross-domain":[20],"multimedia":[21],"information":[22],"during":[23],"inference":[24],"with":[25,88],"continuously":[27],"changing":[28],"data":[29],"distribution.":[30],"Previous":[31],"methods":[32],"have":[33],"been":[34],"found":[35],"to":[36,41,66,79,102,112,124],"lack":[37],"noise":[38,150],"robustness,":[39],"leading":[40],"significant":[43],"increase":[44],"errors":[46],"under":[47],"strong":[48,149],"noise.":[49],"In":[50],"this":[51],"paper,":[52],"we":[53,73,94,116,153],"address":[54],"noise-robustness":[56],"problem":[57],"continual":[59,143],"TTA":[60,144],"by":[61],"offering":[62],"three":[63,140],"effective":[64],"recipes":[65],"mitigate":[67],"it.":[68],"At":[69,90],"category":[71,84],"level,":[72,93],"employ":[74],"Taylor":[76],"cross-entropy":[77],"loss":[78],"alleviate":[80],"low":[82],"confidence":[83],"bias":[85],"commonly":[86],"associated":[87],"cross-entropy.":[89],"sample":[92],"reweight":[95],"target":[97],"samples":[98],"based":[99],"uncertainty":[101],"prevent":[103],"model":[105,127],"from":[106],"overfitting":[107],"noisy":[109],"samples.":[110],"Finally,":[111],"reduce":[113],"pseudo-label":[114],"noise,":[115],"propose":[117],"soft":[119],"ensemble":[120,130],"negative":[121],"learning":[122],"mechanism":[123],"guide":[125],"optimization":[128],"using":[129],"complementary":[131],"pseudo":[132],"labels.":[133],"Our":[134],"method":[135],"achieves":[136],"state-of-the-art":[137],"performance":[138],"widely":[141],"used":[142],"datasets,":[145],"particularly":[146],"setting":[151],"that":[152],"introduced.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
