{"id":"https://openalex.org/W4224917061","doi":"https://doi.org/10.1109/icassp43922.2022.9747767","title":"Depth Removal Distillation for RGB-D Semantic Segmentation","display_name":"Depth Removal Distillation for RGB-D Semantic Segmentation","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4224917061","doi":"https://doi.org/10.1109/icassp43922.2022.9747767"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9747767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747767","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5076512401","display_name":"Tiyu Fang","orcid":"https://orcid.org/0000-0002-4432-1290"},"institutions":[{"id":"https://openalex.org/I34949971","display_name":"University of Jinan","ror":"https://ror.org/02mjz6f26","country_code":"CN","type":"education","lineage":["https://openalex.org/I34949971"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tiyu Fang","raw_affiliation_strings":["University of Jinan,School of Information Science and Engineering,Jinan,China,250022"],"affiliations":[{"raw_affiliation_string":"University of Jinan,School of Information Science and Engineering,Jinan,China,250022","institution_ids":["https://openalex.org/I34949971"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036446937","display_name":"Zhen Liang","orcid":"https://orcid.org/0000-0002-1749-2975"},"institutions":[{"id":"https://openalex.org/I34949971","display_name":"University of Jinan","ror":"https://ror.org/02mjz6f26","country_code":"CN","type":"education","lineage":["https://openalex.org/I34949971"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Liang","raw_affiliation_strings":["University of Jinan,School of Information Science and Engineering,Jinan,China,250022"],"affiliations":[{"raw_affiliation_string":"University of Jinan,School of Information Science and Engineering,Jinan,China,250022","institution_ids":["https://openalex.org/I34949971"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101125772","display_name":"Xiuli Shao","orcid":"https://orcid.org/0000-0001-5059-3172"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiuli Shao","raw_affiliation_strings":["Nankai University,College of Computer Science,Tianjin,China,300350"],"affiliations":[{"raw_affiliation_string":"Nankai University,College of Computer Science,Tianjin,China,300350","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041446903","display_name":"Zihao Dong","orcid":"https://orcid.org/0000-0002-5927-7974"},"institutions":[{"id":"https://openalex.org/I34949971","display_name":"University of Jinan","ror":"https://ror.org/02mjz6f26","country_code":"CN","type":"education","lineage":["https://openalex.org/I34949971"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihao Dong","raw_affiliation_strings":["University of Jinan,School of Information Science and Engineering,Jinan,China,250022"],"affiliations":[{"raw_affiliation_string":"University of Jinan,School of Information Science and Engineering,Jinan,China,250022","institution_ids":["https://openalex.org/I34949971"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100603459","display_name":"Jinping Li","orcid":"https://orcid.org/0000-0002-1626-716X"},"institutions":[{"id":"https://openalex.org/I34949971","display_name":"University of Jinan","ror":"https://ror.org/02mjz6f26","country_code":"CN","type":"education","lineage":["https://openalex.org/I34949971"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinping Li","raw_affiliation_strings":["University of Jinan,School of Information Science and Engineering,Jinan,China,250022"],"affiliations":[{"raw_affiliation_string":"University of Jinan,School of Information Science and Engineering,Jinan,China,250022","institution_ids":["https://openalex.org/I34949971"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076512401"],"corresponding_institution_ids":["https://openalex.org/I34949971"],"apc_list":null,"apc_paid":null,"fwci":0.4198,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.69349779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2405","last_page":"2409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.8454970717430115},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7513543367385864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7346600890159607},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.689323365688324},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5100820660591125},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4875912070274353},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4460964500904083},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3643491864204407},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.288202702999115}],"concepts":[{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.8454970717430115},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513543367385864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7346600890159607},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.689323365688324},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5100820660591125},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4875912070274353},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4460964500904083},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3643491864204407},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.288202702999115}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9747767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747767","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1565402342","https://openalex.org/W1745334888","https://openalex.org/W1817277359","https://openalex.org/W1905829557","https://openalex.org/W1923184257","https://openalex.org/W1923697677","https://openalex.org/W2066813062","https://openalex.org/W2090518410","https://openalex.org/W2103328396","https://openalex.org/W2221101993","https://openalex.org/W2395611524","https://openalex.org/W2485522583","https://openalex.org/W2489780108","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2587989515","https://openalex.org/W2739253758","https://openalex.org/W2799710877","https://openalex.org/W2895401575","https://openalex.org/W2960933010","https://openalex.org/W2963563573","https://openalex.org/W2964309882","https://openalex.org/W3015871565","https://openalex.org/W3035531015","https://openalex.org/W3042346170","https://openalex.org/W3099155473","https://openalex.org/W3108288688","https://openalex.org/W3124434019","https://openalex.org/W3167017747","https://openalex.org/W6605121731","https://openalex.org/W6638480814","https://openalex.org/W6640054144","https://openalex.org/W6640295612","https://openalex.org/W6666899075","https://openalex.org/W6733387591","https://openalex.org/W6748481559","https://openalex.org/W6780728829","https://openalex.org/W6785207073","https://openalex.org/W6787403998"],"related_works":["https://openalex.org/W2486460843","https://openalex.org/W2168109476","https://openalex.org/W1968121071","https://openalex.org/W2061647633","https://openalex.org/W4379231730","https://openalex.org/W2020254986","https://openalex.org/W2686985752","https://openalex.org/W1992540108","https://openalex.org/W2890372105","https://openalex.org/W1522196789"],"abstract_inverted_index":{"RGB-D":[0,19,45,74,107,189],"semantic":[1,20,46,75],"segmentation":[2,21,47,76,88],"is":[3,37,67,95,124,168],"attracting":[4],"wide":[5],"attention":[6],"due":[7],"to":[8,24,40,69,85,97,111,126,131,170],"its":[9],"better":[10],"performance":[11],"than":[12],"conventional":[13],"RGB":[14,32,51,157],"methods.":[15],"However,":[16],"most":[17],"of":[18,44,150,154,175,194],"methods":[22,48],"need":[23],"acquire":[25],"the":[26,54,99,112,121,128,132,142,148,173,176,192],"real":[27],"depth":[28,55,63,71,151],"information":[29],"for":[30,49,102,147],"segmenting":[31,50],"images":[33,52],"effectively.":[34],"Therefore,":[35],"it":[36],"extremely":[38],"challenging":[39],"take":[41],"full":[42],"advantage":[43],"without":[53],"input.":[56],"To":[57],"address":[58],"this":[59],"challenge,":[60],"a":[61,92,160],"general":[62,119,136],"removal":[64],"distillation":[65],"method":[66],"proposed":[68,169,196],"remove":[70],"dependence":[72],"from":[73,106],"model":[77,178],"by":[78,138,156,179],"knowledge":[79,105,130],"distillation,":[80],"which":[81],"can":[82],"be":[83],"employed":[84],"any":[86],"CNN-based":[87],"network":[89,101,123,134,144],"structure.":[90],"Specifically,":[91],"depth-aware":[93,116],"convolution":[94,117],"adopted":[96],"construct":[98],"teacher":[100,122],"getting":[103],"sufficient":[104],"images.":[108,158],"Then":[109],"according":[110],"structure":[113],"consistency":[114],"between":[115],"and":[118,187],"convolution,":[120],"used":[125],"transfer":[127],"learned":[129],"student":[133,143,177],"with":[135],"convolutions":[137],"sharing":[139],"parameters.":[140],"Next,":[141],"makes":[145],"up":[146],"lack":[149],"in":[152],"manner":[153],"learning":[155],"Meantime,":[159],"Variable":[161],"Temperature":[162],"Cross":[163],"Entropy":[164],"(VTCE)":[165],"loss":[166],"function":[167],"further":[171],"increase":[172],"accuracy":[174],"soft":[180],"target":[181],"distillation.":[182],"Extensive":[183],"experiments":[184],"on":[185],"NYUv2":[186],"SUN":[188],"datasets":[190],"demonstrate":[191],"superiority":[193],"our":[195],"approach.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}