{"id":"https://openalex.org/W4415708158","doi":"https://doi.org/10.1109/icme59968.2025.11209937","title":"DLLM: Enhancing Open-World Object Detection with Dynamic Learning and Large Models","display_name":"DLLM: Enhancing Open-World Object Detection with Dynamic Learning and Large Models","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708158","doi":"https://doi.org/10.1109/icme59968.2025.11209937"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11209937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5110830089","display_name":"Yangyang Huang","orcid":"https://orcid.org/0009-0001-8694-5755"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangyang Huang","raw_affiliation_strings":["South China University of Technology,School of Computer Science,GuangZhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Computer Science,GuangZhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027725869","display_name":"Xing Xi","orcid":"https://orcid.org/0000-0003-0363-6656"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Xi","raw_affiliation_strings":["South China University of Technology,School of Computer Science,GuangZhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Computer Science,GuangZhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101563319","display_name":"Ronghua Luo","orcid":"https://orcid.org/0000-0001-8629-3323"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ronghua Luo","raw_affiliation_strings":["South China University of Technology,School of Computer Science,GuangZhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Computer Science,GuangZhou,China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27770131,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":0.6917999982833862,"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":0.6917999982833862,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.17870000004768372,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.01860000006854534,"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/inference","display_name":"Inference","score":0.652899980545044},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6173999905586243},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6172999739646912},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5968000292778015},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.576200008392334},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5526000261306763},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5182999968528748},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4771000146865845}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7487999796867371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7312999963760376},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.652899980545044},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6173999905586243},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6172999739646912},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5968000292778015},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.576200008392334},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5526000261306763},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5182999968528748},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49410000443458557},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.445499986410141},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4169999957084656},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3537999987602234},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.3440999984741211},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2827000021934509},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11209937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209937","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2296073425","https://openalex.org/W3001083904","https://openalex.org/W3176287975","https://openalex.org/W3176709420","https://openalex.org/W3217535672","https://openalex.org/W4312210066","https://openalex.org/W4313018996","https://openalex.org/W4327852039","https://openalex.org/W4377235163","https://openalex.org/W4386075915","https://openalex.org/W4386076022","https://openalex.org/W4390874575","https://openalex.org/W4402592316","https://openalex.org/W4402727951"],"related_works":[],"abstract_inverted_index":{"Open":[0],"World":[1],"Object":[2],"Detection":[3],"(OWOD)":[4],"often":[5,47],"uses":[6],"objectness":[7],"scores":[8],"as":[9],"a":[10,70],"key":[11],"metric":[12],"for":[13,87],"identifying":[14],"unknown":[15,88,96,111],"objects,":[16],"which":[17,56],"leads":[18,119],"to":[19,33,52,58,60,78,93,128],"label":[20],"bias":[21],"issues.":[22],"To":[23],"address":[24],"this":[25,80],"problem,":[26],"we":[27,68,83],"applied":[28],"Unsupervised":[29,73],"Domain":[30,74],"Adaptation":[31,75],"(UDA)":[32],"OWOD":[34],"and":[35,118],"built":[36],"an":[37],"unbiased":[38],"foreground/background":[39],"predictor.":[40],"However,":[41],"current":[42],"self-training":[43],"methods":[44,109],"in":[45,110,120],"UDA":[46],"rely":[48],"on":[49,130],"fixed":[50],"thresholds":[51],"calculate":[53],"unsupervised":[54],"loss,":[55],"fails":[57],"adapt":[59],"the":[61],"learning":[62],"di\ufb03culties":[63],"of":[64],"different":[65],"categories.":[66],"Thus,":[67],"propose":[69],"Dynamic":[71],"Learning-driven":[72],"(DLDUA)":[76],"strategy":[77],"meet":[79],"challenge.":[81],"Furthermore,":[82],"generate":[84],"initial":[85],"pseudo-labels":[86],"objects":[89],"using":[90],"large":[91],"models":[92],"recall":[94,113],"more":[95],"objects.":[97],"Experimental":[98],"results":[99],"show":[100],"that":[101],"our":[102,136],"method":[103,137],"significantly":[104],"outperforms":[105],"existing":[106],"state-of-the-art":[107],"(SOTA)":[108],"object":[112],"(improving":[114],"by":[115,123],"15.2":[116],"U-Recall)":[117],"inference":[121],"speed":[122],"over":[124],"10":[125],"FPS":[126],"compared":[127],"based":[129],"deformable":[131],"DETR":[132],"SOTA":[133],"methods.":[134],"Additionally,":[135],"accelerates":[138],"model":[139],"convergence.":[140]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-30T00:00:00"}