{"id":"https://openalex.org/W4403791852","doi":"https://doi.org/10.1145/3664647.3680613","title":"Advancing Semantic Edge Detection through Cross-Modal Knowledge Learning","display_name":"Advancing Semantic Edge Detection through Cross-Modal Knowledge Learning","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791852","doi":"https://doi.org/10.1145/3664647.3680613"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3680613","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd 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/A5004212956","display_name":"Ruoxi Deng","orcid":"https://orcid.org/0000-0001-5912-0152"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruoxi Deng","raw_affiliation_strings":["Wenzhou Institute of Data Research School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China"],"affiliations":[{"raw_affiliation_string":"Wenzhou Institute of Data Research School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100954087","display_name":"Bin Yu","orcid":"https://orcid.org/0009-0009-8998-423X"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Yu","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102060503","display_name":"Jinxuan Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinxuan Lu","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100528039","display_name":"Caixia Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caixia Zhou","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065549500","display_name":"Zhao-Min Chen","orcid":"https://orcid.org/0000-0001-9492-0796"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao-Min Chen","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100679578","display_name":"Jie Hu","orcid":"https://orcid.org/0000-0003-3296-5459"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Hu","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China","institution_ids":["https://openalex.org/I146620803"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5004212956"],"corresponding_institution_ids":["https://openalex.org/I146620803"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18522188,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4524","last_page":"4532"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9922000169754028,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9922000169754028,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9868999719619751,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9835000038146973,"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":0.7340379953384399},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6564337611198425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46665358543395996},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.37407898902893066},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.07317569851875305}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7340379953384399},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6564337611198425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46665358543395996},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37407898902893066},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.07317569851875305},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3680613","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680613","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W845365781","https://openalex.org/W1861492603","https://openalex.org/W2016396776","https://openalex.org/W2071356085","https://openalex.org/W2144794286","https://openalex.org/W2145023731","https://openalex.org/W2150134853","https://openalex.org/W2187089797","https://openalex.org/W2340897893","https://openalex.org/W2883254424","https://openalex.org/W2885139206","https://openalex.org/W2963307106","https://openalex.org/W2982631194","https://openalex.org/W3093425017","https://openalex.org/W3207146952","https://openalex.org/W3216325077","https://openalex.org/W4386065733","https://openalex.org/W4386134471","https://openalex.org/W4390874575","https://openalex.org/W4390889814"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Semantic":[0],"edge":[1],"detection":[2],"(SED)":[3],"is":[4],"pivotal":[5],"for":[6],"the":[7,20,40,66,72,95,100,107,126],"precise":[8],"demarcation":[9],"of":[10,22,42,109,128,135],"object":[11],"boundaries,":[12],"yet":[13],"it":[14],"faces":[15],"ongoing":[16],"challenges":[17],"due":[18],"to":[19,36,93],"prevalence":[21],"low-quality":[23],"labels":[24],"in":[25,74,146],"current":[26],"methods.":[27],"In":[28],"this":[29,82],"paper,":[30],"we":[31,85],"present":[32],"a":[33,143],"novel":[34],"solution":[35],"bolster":[37],"SED":[38,114,147],"through":[39],"encoding":[41,81],"both":[43,156],"language":[44,68],"and":[45,77,99,112,123,137,158],"image":[46,76,97],"data.":[47],"Distinct":[48],"from":[49],"antecedent":[50],"language-driven":[51],"techniques,":[52],"which":[53],"predominantly":[54],"utilize":[55,90],"static":[56,157],"elements":[57],"such":[58],"as":[59],"dataset":[60],"labels,":[61],"our":[62,129],"method":[63],"taps":[64],"into":[65],"dynamic":[67,159],"content":[69],"that":[70,89],"details":[71],"objects":[73],"each":[75],"their":[78],"interrelations.":[79],"By":[80],"varied":[83],"input,":[84],"generate":[86],"integrated":[87],"features":[88,98,111],"semantic":[91],"insights":[92],"refine":[94],"high-level":[96],"ultimate":[101],"mask":[102],"representations.":[103],"This":[104],"advancement":[105,145],"improves":[106],"quality":[108],"these":[110],"elevates":[113],"performance.":[115],"Experimental":[116],"evaluation":[117],"on":[118],"benchmark":[119],"datasets,":[120],"including":[121],"SBD":[122],"Cityscape,":[124],"showcases":[125],"efficacy":[127],"method,":[130],"achieving":[131],"leading":[132],"ODS":[133],"F-scores":[134],"79.0":[136],"76.0,":[138],"respectively.":[139],"Our":[140],"approach":[141],"signifies":[142],"notable":[144],"technology":[148],"by":[149],"seamlessly":[150],"integrating":[151],"multimodal":[152],"textual":[153],"information,":[154],"embracing":[155],"aspects.":[160]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}